Post on 06-Feb-2023
Ministry of Higher Education and Scientific Research Al-Mustanseriyah University College of Education
Enhancement of Color Images Captured at Different Lightening
Conditions
A Thesis Submitted to the Council of the Physics Department, College of
Education, Al-Mustansiriyah University in Partial Fulfillment of the Requirements for the Degree of Doctor Philosophy in Physics
By
Hazim Gati' Dway Al-Khuzai
(B.Sc.2001) (M.Sc.2004)
Supervised by
2011 A.D. 1432 A.H.
Dr. Radhi Sh.Hamoudi Al-Taweel Assit. Prof
Dr. Ali A . Al – Zuky Assit. Prof
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DEDICATION
This thesis is dedicated to my mother soul with mercy. Also, this thesis is dedicated to my father, my wife and my brothers and sisters who believe in the richness of learning.
Hazim
Acknowledgments
First, praise be to Allah, Full of Majesty for giving
me the health, and support throughout my research. I would like to express my deep thanks and gratitude
to my supervisors, Dr. Ali A . Al – Zuky and Dr. Radhi Sh. Hamoudi Al – Taweel for suggesting the topic of this research, and their assistance throughout the course of this work.
I am grateful to the staff members of physics department in the college of education Al-Mostanseriyah University.
I would like to express my deep gratitude to my family.
My thanks go to postgraduate colleagues and all friends of the physics department.
Hazim
ii
Abstract
The images with good lightness and contrast are a strong requirement in
several areas of applications such as good vision, remote sensing, medical
and aerial image enhancement and the analysis of such images by
measuring the quality is very important for this applications. In this study
first, we have suggested new criteria to determine the quality of the color
image based on changing in lightness and contrast called Quality Factor
(QF), where the change in the lightness and contrast was done by
controlling the illuminance levels of the captured images. We used the
LEDs lighting system to generate the illuminance which is graded from low
to moderate levels and MH lighting system to generate the illuminance
which is graded from moderate to high illuminance levels and also we
study the distribution of illuminance in the two systems. Six groups of the
color images (150 images) have been captured under different illuminance
levels are analyzed by using many methods of image quality assessments;
like, the mean of locally (µ ,σ ) model , CM , EFD and SSIM, then we
compared these methods with suggested method QF.
Second, we introduced two methods to enhance color images based on
changing in the lightness and contrasts, the named Modified Retinex (MR)
and Adaptive Histogram Equalization (AHE). These algorithms have been
compared with the other algorithms like (MSRCR, HE and AINDANE).
From the test results, it is noted that the QF assessment is a robust method
to determine quality of images with different lightness level from low to
high. The MR and MSRCR are good algorithms to enhance the color
images with low and moderate lightness levels compared with others
algorithms, whereas the images with high lightness levels, namely the MR
and AHE algorithm are best methods to enhance these images.
iii
AC Alternating Current AHE Adaptiv histogram equalization AINDANE Adaptive Integrated Neighborhood Dependent Approach for Nonlinear Enhancement ANSI American National Standards Institute BRDF Bi-Directional Reflectance Distribution Function CCT Correlated Color Temperature CIE Commission International de l’´Eclairage CIELAB Color Space CIELUV Color Space CIE XYZ Color Space CM Colorfullness Metrics CRI Color Rendering Index CRT Cathode Ray Tube CSF Contrast Sensitivity Function EFD Entropy of the First Derivative Image HE Histogram Equalization HSV Color Space JPEG Joint Photographic Experts Group LED Lighting Emission Diode LEDs Lighting Emission Diodes MH Metal Halide MOS Mean Opinion Score MR Modified Retinex algorithm MSE Mean Squared Error MSR Multi Scale Retinex algorithm MSRCR Multi Scale Retinex Algorithm with Color Resroration NASA National Aeronautics and Space Administration NTSC National Television Standards Committee PDF Probability Density Function PSNR Peak Signal to Noise Ratio QF Quality Factor R,G,B Red, Green, Blue RGB Color Space SPD Spectral Power Distribution SSIM Structural Similarity Index SSR Signal Scale Retinex YIQ Color Space
Abbreviations
Symbol Definition
iv
Acknowledgments …………………………………………………… i Abstract ……………………………………………………………… ii List of Abbreviation …………………………………………………. iii List of Contents ………………………………………………………. iv
1-7
1.1 Introduction …………………………………………………… … 1 1.2 Challenges ………………………………………………………… 2 1.3 Literature Review ……………………………………………..… 3 1.4. Research Aim …...…………………………………………. ……. 6 1.3. Structure of thesis ..…………………………………… ………… 6
2.1 Introduction ………….. …………………………………………… 8 2.2 Light ……………………………………………………………….. 8 2.2.1 Light and Materials ……………………………………………… 8 2.2.2 Artificial light sources ………………………………………….. . 11 2.2.3 Lighting quality …………………………………………………... 13 2.3 Human visual system ………………………………………………. 15 2.3.1 The human eye …………………………………………………... 16 2.3.2 Visual sensitivity ………………………………………………... 17 2.3.3 Contrast ………………………………………………………….. 17 2.3.5 The Contrast Sensitivity Function ……………………………….. 18 2.3.6 Adaptation …………………………………….………………….. 19 2.3.7 Brightness perception ……………………………………………. 20 2.3.8 Lightness and color constancy ………………………………….. 20 2.4 Color space …………………………………………………………. 21 2.4.1 CIE chromaticity system ………………………………………... 21 2.4.2 RGB color space …………………………………………………. 23 2.4.3 YIQ color model ………………………………………………… 24 2.4.4 HSV Color Model ……………………………………………….. 25
Subject Pages
List of Contents
Chapter One …… General Introduction 1-7
Chapter Two …… Theoretical Principle 8-28
v
2.4.5 CIELAB COLOR SPACE ………………………………………. 27 3.1 Introduction ……………………………………………………..… 29 3.2 Image quality Measurement ……………………………………..… 29 3.2.1 Subjective Quality Measurement……………..………………..… 29 3.2.2 Objective Quality Measurement…………...…………………..…. 30 3.3 Image Quality assessment………………………………………….. 31 3.3.1 The Structural Similarity Index ………………………………….. 32 3.3.2 The entropy of the first derivative image………………………… 34 3.3.3 The Mean of locally (µ ,σ ) model …….………………………. 35 3.3.4 Color fullness metrics………………...…………………………… 36 3.4 Contrast and lightness enhancement algorithms ….…………….. …..36 3.4.1 Histogram equalization……………………………………..…….. 36 3.4.2 Multi scale retinex algorithm….………………………………….. 38 3.4.2 AINDANE algorithm……………………………………………... 39 4.1 Introduction …………………………………...………………….. 44 4.2 Lighting systems and images features ………………………….. 44 4.2.1. LEds Lighting system…………………………………………… 44 4.2.2 MH lighting system ……………………………….…………… .. 46 4.3 Adaptive methods in the Objective Quality based on lightness changing ……………………………………………………………… 46 4.4 Contrast and lightness enhancement algorithms ……...……………. 49 4.4.1 Adaptive histogram equalization (AHE) algorithm …………… 49 4.4.2 Modify retinex (MF) algorithm ……………………………...….. 51
5.1 Introduction ….……...…………………………………………... 54 5.2 Determining the Distribution of illuminance …………………… 54 5.2.1 Illuminance Distribution in the LEds Lighting system...………. 54 5.2.2 Illuminance Distribution in the MH lighting system……...…… 66 5.3 Results of the Images quality assessments …………….………… 76 5.3.1 The entropy of the first derivative image (EFD) …………........ 76
Chapter Three …. Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast 29-43
Chapter FOUR ... Lighting Systems and Suggested Algorithms of image Quality Qssessment and Enhancement 44-53
Chapter Five ... The Results And Discussion 54-110
vi
5.3.2 The Mean of locally (µ ,σ ) model ……………………………...... 76 5.3.3 Colorfullness metrics CM ……..………………………………… 81 5.3.4 Quality factor (QF) assessment ………………………….……... 81 5.3.5 Stricture similarity index ………………………………………... 81 5.4 The results of images enhancement ……………………………….. 89 5.4.1Color images enhancement with low and moderate
lightness levels …………………………………..... 89
5.4.2 Color images enhancement with moderate and high lightness levels ……………………………………... 96 Conclusions ………………………………………………...……… 112 Suggestion Future Works …………………………………..……… 113 References …………………………………………………..……… 114
Chapter Six ... The Conclusion and Suggested Future Work 111-112
Chapter One: General Introduction
1
1.1 Introduction
Digital image processing is the technology of applying a number of computer
algorithms to process digital images. The outcomes of this process can be either
images or a set of representative characteristics in properties of the original
images. The applications of digital image processing have been commonly
found in robotics/intelligent system, medical imaging, remote sensing,
photography and forensics. The development of computer has led to developing
the science of image processing. At the beginning of the 20P
thP century, it was
common to use small colored image P
, Pbecause of the development of computer
system and its great ability to store, there was a wide range to use colored image
that has a high clarity and big sizes including colored image processing. Digital
image processing can be divided generally into three main categories [1]:
a. Image Enhancement and Restoration.
b. Image Coding and Compression.
c. Image Segmentation and Description.
Statically analysis plays an important role in various image processing
applications in the above groups to know image quality, for example, the
processed image results from enhancement, compression and coding needs to be
assess its quality, however, this done by using mathematical analysis . Image
enhancement by reducing the noise to a minimal level is one of the most
fundamental research topics in image processing. Different types of noise as
additive or multiplicative noise are initiated during the process of acquisition to
digitization of an image, causing degradation in quality, but there is important
element can affect the image illumination that incident on the scene in real
world. The level, type, distribution and uniformity of the illumination can be
determine the image quality hence, various image processing techniques have
been developed to recover the meaningful information under changing lighting
conditions. Among these, the algorithms based on integrated neighborhood
Chapter One: General Introduction
2
dependency of pixel characteristics and those based on the illumination
reflectance model which perform well for enhancing the visual quality of digital
images captured under nonuniform (extremely low and high) lighting
conditions.
1.2 Challenges
A common and often serious discrepancy exists between recorded color
images and the direct observation of scenes. Human perception excels at
constructing a visual representation with vivid color and detail across the wide
range of photometric levels due to lighting variations. In addition, human vision
computes color so as to be relatively independent of spectral variations in
illumination [2]. Therefore the images taken from a camera or displayed on
monitors/display devices suffer from certain limitations. As a result, bright
regions of the image may appear overexposed and dark regions may appear
underexposed. The contrast and lightness enhancement are necessary to improve
the visual quality of images. But color image enhancement (outcome to the
noise or insufficient lighting) can cause:
a. High smoothing in the edges region (edges distortion).
b. Color shift or false.
c. Halo effect.
Many researchers [2-5] have enhanced color image based on insufficient
lightning and determined images quality by using their subjective quality
measurement without using any objective measurement. This method does not
represent a good criterion to determine the efficiency of the enhancement, hence
it is has better use of the objective measurements or the subjective
measurements with large sample of persons, but the two manners have
drawback, especially the subjective measurements that is affected by displays
Chapter One: General Introduction
3
tools, illumination of the environment and other personal factors. The crucial
difficulty in image quality assessment depending on the objective measurements
is no reference or optimal image that is used to determine quality of the
enhancement image. No reference image quality is useful to many still image
applications as assessment equality of high resolution image, JPEG image
compressed [6]. Moreover, measured image quality depending on variety of
lightness or image enhancement that results from insufficient lightness.
1.3 Literature Review
The previous work in color image processing techniques enhancement based
on lightness change. And image analysis to determine its quality will be given
with a brief description to each of them as following:
Eli Peli (1990): proposed method for contrast enhancement dependent on the
physical contrast of simple images such as sinusoidal gratings or a single patch
of light on a uniform background which is well defined and agrees with the
perceived contrast [7].
D. J. Jobson etal (1996): introduced new algorithm to improve the brightness,
contrast and sharpness of an image. It performs a non-linear spatial/spectral
transform that provides simultaneous dynamic range compression P
P[8], and in
1997 they compared this method with another enhancement technique such as
histogram equalization homomorphic filtering P
P[9].
B. V. Funt etal (1997): introduced investigations into Multi-Scale Retinex
algorithm approach to image enhancement to explain the effect of the processing
from a theoretical standpoint P
P[10]. In the same year they modified the multi-
scale retinex approach to image enhancement so that the processing is more
justified from a theoretical standpoint by suggested a new algorithm with fewer
arbitrary parameters that is more flexible P
P[11].
Chapter One: General Introduction
4
D.J.Jabson etal (2002): suggested no reference image quality assessment, by
proposing the idea that good visual representations seem to be based upon some
combination of high regional visual lightness and contrast depending on the
value of mean and a standard deviation locally [12].
D. Hasler and S. Susstrunk (2003): they measured colorfulness meters in
natural images in CIELAB color space depending on the trigometric length of
the standard deviation and mean in A, B components [13].
Ayten N. Al-Biaty (2005): she evaluated image quality depending on computing
the image contrast in edge regions, and introduces quantitative measures to
determine image quality, and then estimated the efficiency of the various
techniques in image processing applications. In her work suggested new
techniques to calculate image contrast and studying it as a function of number of
smoothing iterations from using mean filter and a function of gray level
resolution [14].
Li Tao and Vijayan K. Asari(2005): they introduced AINDANE (Adaptive and
integrated neighborhood dependent approach for nonlinear enhancement) of
color images neighborhood algorithm to improve the visual quality of digital
images captured under extremely low or uniform lightening conditions. It
consists of two main parts: luminance enhancement and contrast enhancement P
P[4].
Li Tao etal (2006): they proposed image enhancement technique which has
been developed to improve the visual quality of digital images that exhibit dark
shadows due to the limited dynamic ranges of imaging and display devices
which are incapable of handling high dynamic range scenes. The proposed
technique processes images into two separate steps: dynamic range compression
and local contrast enhancement. Dynamic range compression is a neighborhood
dependent intensity transformation which is able to enhance the luminance in
Chapter One: General Introduction
5
dark shadows while keeping the overall tonality consistent with that of the input
image [15].
Osman Nuri and Capt Ender (2007) : proposed a new algorithm to enhance
night scenes and under nonuniform lighting conditions, either the low intensity
areas or the high intensity areas cannot be clearly seen depending on non linear
transformP
P[16].
Ali J. Al Dalawy (2008): studied the TV-Satellite images of "Al-Hurra" channel
broadcasted on Arabsat, Hotbird and Nilesat. These images were the same with
respect to the type on the three satellites. Analyzing these images done
statistically by finding the statistics distribution and studying the relations
between the mean and the standard deviation of the color compound (RGB) and
light component for the image as whole and for the extracted homogeneous
regions. Also he studied the contrast of image edges depending on sobel
operator in limiting the edges and studied the contrast as function for edge
finding threshold. He found the Hotbird has the best results [17].
Thuy Tuong etal (2008): proposed method to determine no reference image
quality by calculating the entropy of the first derivative of the lightness
component and evolute probability of the edges region [18].
Salema S. Salman(2009) studied the effect of different lighting operations in
type and intensity on test images using different light sources (tungsten and
fluorescent lamp) and studied the distribution homogeneity of light intensity for
the line partitioned from the middle of white test image width and height and
focused on the contrast ratio as a function for light intensity using a test image
with one half white and the other half black [19].
W.S. Malpica, A.C. Bovik (2009): they suggested full Image Quality
Assessment using the structural similarity index, this method requires two
Chapter One: General Introduction
6
images (optimal and original image) and then evaluation of the three different
measures, the luminance, contrast, and structure comparison is made [20].
Qieshi Zhang etal (2010): they presented an adaptive histogram separation and
mapping method for Backlight image enhancement the proposed adaptive
histogram separation unit .And then mapping the low dynamic range histogram
partition into high dynamic range . By doing this, the excessive or scarcity
enhancement can be avoid. The results show that the proposed method can gives
better enhancement if it compared with some histogram analyzing based
methods [21].
Diana C. Gil etal (2011): They introduced contrast enhancement methods and
Image quality is evaluated by means of objective metrics such as intensity
contrast and brightness error, and by subjective assessment. Execution time is
also measured. They found the technique based on histogram modification
presents a better trade-off considering both aspects [22].
1.4 Research Aim
There are two main purposes of the present study: first, analyzing color images
by using many algorithms which are captured under different illuminance
(lightness and contrast ) levels by using two types of lighting systems (LEDs
and MH) and study the distribution of the illuminance in the two system. Where
the quality of these images have been determined using mean of locally (µ, σ)
model, Structural Similarity Index (SSIM), entropy of the first derivative image
(EFD) and colorfulness metrics (CM). Moreover, we suggested new method
"called Quality Factor QF" . After knowing the effect of illuminance degree on
the image, the second goal is enhance, these images rely on many algorithms;
Chapter One: General Introduction
7
like, histogram equalization and multi scale retinex algorithm with color
restoration (MSRCR).
1.5 Structure of thesis
The organization of the rest of the thesis is as follows: chapter two details
all the theoretical principles of the light , human visual system and color.
The Color Image Analysis and Enhancement based on lightness and contrast
change are introduced in the chapter three. Chapter four includs the lighting
systems and the suggested algorithms of image quality assessment and
enhancement. The fifth chapter gives results and discussion, Conclusions and
future works are also given in chapter six.
Chapter Two: Theoretical Principles
8
2.1 Introduction Color in images is usually represented by a tri band signal, for instance Red
Green and Blue (R, G, B) this signal is sensitive to changes in illumination and
in real world the image is formed when light energy is scattered by surfaces in
the scene towards the viewport and it is the product of the shape, reflectance and
illumination in a scene. Many theoretical concepts play important role in the
image formation, analysis and enhancement in this chapter we focus on many
concepts such as light, color space and human visual system.
2.2 Light Light is just one portion of the various electromagnetic waves flying
through space. These waves have both a frequency and a length, the values of
which are distinguishing light from other forms of energy on the
electromagnetic spectrum. Light is emitted from a body due to Incandescence,
Electric Discharge, Electro luminescence and Photoluminescence [23]. Images
cannot exist without light. To produce an image, the scene must be illuminated
with one or more light sources. In this section we focus on interaction of light
with surface and some artificial light source. Moreover we determine general
factors that affect on the light equality assessment.
2.2.1 Light and Materials A Radiometry is the science of measuring light from any part of the
electromagnetic spectrum. In general, the term usually applies to the
measurement using optical instruments of light in the visible, infrared and
ultraviolet wavelength regions. The terms and units have been standardized in
the American National Standards Institute publications (ANSI ) [24], whereas
the Photometry is the science of measuring light within the visible portion of the
electromagnetic spectrum in units weighted in accordance with the sensitivity of
the human visual system [25]. Light is a form of radiometric energy, radiometry
is used in graphics to provide the basis for illumination calculations which are
Chapter Two: Theoretical Principles
9
(Radiant power, Radiant Intensity, Irradiance and Radiance) and the
corresponding in Photometry units are (Luminous flux, Luminous intensity,
Illuminance and Luminance) [26]. If we addressed in the simulation of light
distribution it involves characterizing the reflections of light from surfaces.
Various materials reflect light in very different ways, for example matt house
paint reflects light many differently than the often highly specular paint used on
a sports car. Reflection is the process whereby light of a specific wavelength is
(at least partially) propagated outward by a material without change in
wavelength, or more precisely, “reflection is the process by which
electromagnetic flux (power), incident on a stationary surface or medium, leaves
that surface or medium from the incident side without change in frequency;
reflectance is the fraction of the incident flux that is reflected”, [27]. The effect
of reflection depends on the directional properties of the surface involved. The
reflective behavior of a surface is described by its Bi-Directional Reflectance
Distribution Function (BRDF).
The BRDF expresses the probability that the light coming from a given
direction will be reflected in another direction [28] as shown in figure (2.1).
Generally we define BRDF as the ratio of the reflected intensity (𝐼𝐼𝑣𝑣 radiance) to
the radiation flux (Eir irradiance) in the incident beam [28]:
𝑅𝑅(𝜃𝜃𝑖𝑖 ,𝜙𝜙𝑖𝑖 ,𝜃𝜃𝑒𝑒 ,𝜙𝜙𝑒𝑒) = 𝐼𝐼𝑣𝑣(𝜃𝜃𝑖𝑖 ,𝜙𝜙𝑖𝑖 ,𝜃𝜃𝑒𝑒 ,𝜙𝜙𝑒𝑒)𝐸𝐸𝑖𝑖r (𝜃𝜃𝑖𝑖 ,𝜙𝜙𝑖𝑖)
(2.1)
Figure 2.1: Geometry of the BRDF.
Chapter Two: Theoretical Principles
10
This relates the light in the direction (𝜃𝜃𝑖𝑖 ,𝜙𝜙𝑖𝑖) to outgoing light in the
direction (𝜃𝜃𝑒𝑒 ,𝜙𝜙𝑒𝑒) , Where:
𝐸𝐸ir (𝜃𝜃𝑖𝑖 ,𝜙𝜙𝑖𝑖) = 𝐼𝐼𝑣𝑣(𝜃𝜃𝑖𝑖 ,𝜙𝜙𝑖𝑖) 𝑐𝑐𝑐𝑐𝑐𝑐 𝜃𝜃𝑖𝑖 𝑑𝑑𝑑𝑑 (2.2)
dω is the solid angle .
Figure (2.2) shows different types of material behavior, which are defined as
follows [29]:
• Specular (mirror): Specular materials reflect light in one direction only,
the mirror direction. The outgoing direction is in the incident plane and
the angle of reflection is equal to the angle of incidence.
• Diffuse: Diffuse, or Lambertian materials reflect light equally in all
directions. Reflection of light from a diffuse surface is independent of
incoming direction. The reflected light is the same in all directions and
does not change with viewing angle.
• Mixed: Reflection is a combination of specular and diffuse reflection.
Overall reflectance is given by a weighted combination of diffuse and
specular components.
• Retro-Reflection: Retro-Reflection occurs when the light is reflected
back on itself that is the outgoing direction is equal, or close to the
incident direction. Retro reflective devices are widely used in the areas of
night time transportation and safety.
• Gloss: Glossy materials exhibit the property that involves mixed
reflection and is responsible for a mirror like appearance of a rough
surface.
Figure 2.2: Types of Reflection in the surface of Materials.
Chapter Two: Theoretical Principles
11
Most materials do not fall exactly into one of the idealized material
categories described above, but instead exhibit a combination of specular and
diffuse characteristics and real materials generally have a more complex
behavior, with a directional character resulting from surface finish and sub-
surface scattering [29].
2.2.2 Artificial Light Sources There are many different types of lamps for everyday lighting and for
color imaging lighting. Many of the major categories for everyday lighting are
incandescent, tungsten halogen, fluorescent, mercury, metal halide, sodium and
Lighting Emission Diodes (LEDs). For color imaging (photography), the major
category is the electronic flash lamp.
Two general characteristics of lamps that are important for color imaging are
their spectral power distribution as a function of their life time and operating
conditions. The light output of a lamp decreases during its life. Also, the spectral
power distribution of a tungsten lamp depends on the voltage at which it is
operated. Therefore, for critical color calibration or measurement, we cannot
always assume that the spectral power distribution of a lamp will remain the
same after hours of use or at various operating temperatures [30]. In this study
we used two types of artificial light sources are white LEDs and metal halide
which are described as follows:
a. Light Emitting Diode (LED):
The basic operating principle behind light emitting diodes is inducing
conduction by negatively charged carriers (n-type) and some by positively
charged carriers (p-type). When charged carriers of different types recombine
the energy released may be emitted as light [31]. LED lamps are the newest
addition to the list of energy efficient light sources. While LED lamps emit
visible light in a very narrow spectral band, they can produce "white light". This
is accomplished with either a red-blue-green array or a phosphor-coated blue
LED lamp. LED lamps have made their way into numerous lighting applications
Chapter Two: Theoretical Principles
12
including exit signs, traffic signals, under-cabinet lights, and various decorative
applications. Though still in their infancy, LED lamp technologies are rapidly
progressing and show promise for the future [32].
b. Metal-halide (MH)
It is discharge lamps which are high-pressure mercury lamps with a clear
bulb. In the discharge, tube are added, in addition to the mercury, also different
halide compounds of rare earth metals. The spectra emitted from the added rare
earth metal vapors improve the color, color rendering and the efficacy of the
original high-pressure mercury lamps. Metal-halide discharge lamps are used for
lighting of large sized sports stadiums, squares, etc., where whitish color and
good color rendering is necessary [32]. Table (2.1) shown specification of white
LEDs and MH lamp, in this table we can see some optical and electrical
properties, whereas figure (2.3) demonstrated relative spectral distribution of
them compared with the daylight at D65(white light at 6500k). In this figure the
distribution of white LEDs lamp fairly near the distribution of the daylight.
MH lamp White LEDs lamp Light source BTE40/Italy 3W4CH/340/China model
4100 k 3500k Color temp. 70 75 CRI
100 lm/W 30 lm/W Efficacy 220 v 3 v Average voltage
1000 W 0.7 W for one chip Average Power
Table 2.1: some properties of White LEDs and MH
Chapter Two: Theoretical Principles
13
2.2.3 Lighting Quality What does lighting quality mean? There is no complete answer to the
question. Lighting quality depends on several factors. It depends largely on
people’s expectations and past experiences of electric lighting and Lighting
quality cannot be expressed simply in terms of photometric measures nor
can there be a single universally applicable recipe for good quality lighting [36],
many quality issues are addressed in this section as following:
Visual performance [36]: Is one of the major aspects of the lighting
practice and recommendations is to provide adequate lighting for people to
carry out their visual tasks. Visibility is defined by our ability to detect objects
or signs of given dimensions, at given distances and with given contrasts of the
background. Visual performance is defined by the speed and accuracy of
performing a visual task and visual performance models are used to
evaluate the interrelationships between visual task performance, visual target
size and contrast, observer age and luminance levels Light levels that are
optimized in terms of visual performance should guarantee that the visual
performance can be carried out well above the visibility threshold limits.
Visual performance is improved with increasing luminance. Yet, there is a
plateau above which further increases in luminance do not lead to
Figure 2.3: Relative Spectral Power distribution of MH
[33] and white LEDs lamp [34] ,and daylight[35].
Chapter Two: Theoretical Principles
14
improvements in visual performance. Thus increasing luminance levels above
the optimum for visual performance may not be justified and can on the
contrary lead to excessive use of energy. The visual performance aspect and
consumption of electricity for lighting should be in balance in order to increase
energy efficiency as shown in figure (2.4).
Color characteristics: The color characteristics of light in space are determined
by the spectral power distribution (SPD) of the light source and the
reflectance properties of the surfaces in the room. The color of light
sources is usually described by two properties, namely the correlated color
temperature (CCT) and general color rendering index (CRI) [36]. CRI is the
evaluation of how the color looks in a given light source as compared to a
reference source. The spectral composition of different lamps is different and the
sample may reflect different wave lengths and look different. Special CRI can
be measured by Commission Internationale de l’´Eclairage (CIE) by [30]:
𝑅𝑅𝑖𝑖 = 100 – 4.6𝑑𝑑𝐸𝐸𝑖𝑖 (2.3)
Where 𝑑𝑑𝐸𝐸𝑖𝑖is the distance in the CIELUV between colors coordinates of the test.
Figure 2.4: Relative visual performance as a function of background
luminance and target contrast [36].
Chapter Two: Theoretical Principles
15
The CIE general color-rendering index 𝑅𝑅𝑎𝑎 is defined as the arithmetic mean of
the eight CIE special color-rendering indices 𝑅𝑅𝑖𝑖 for the eight standard test-color
samples[30], i.e.,
𝑅𝑅𝑎𝑎 = 18
∑ 𝑅𝑅𝑖𝑖8𝑖𝑖=1 (2.4)
Uniformity of lighting [36]: Uniformity of lighting in space can be desirable or
less desirable depending on the function of the space and type of activities. A
completely uniform space is usually undesirable whereas too nonuniform
lighting may cause distraction and discomfort. Lighting standards and codes
usually provide recommended illuminance ratios between the task area and
its surroundings. Most indoor lighting design is based on providing levels
of illuminances while the visual system deals with light reflected from
surfaces i.e. luminances. For office lighting there are recommended
luminance ratios between the task and its immediate surroundings.
Glare [36]: Is caused by high luminances or excessive luminance differences in
the visual field. Disability glare and discomfort glare are two types of glare, but
in indoor lighting the main concern is about discomfort glare. This is visual
discomfort in the presence of bright light sources, luminaries, windows or
other bright surfaces.
Flicker [36] Is produced by the fluctuation of light emitted by a light
source. Light sources that are operated with AC supply, produce regular
fluctuations in light output. The visibility of these fluctuations depends on the
frequency and modulation of the fluctuation.
2.3 Human Visual System (HVS) Perception is the process that enables humans to make sense of the stimulus
that surrounds them. HVS can be divided into two main parts, the eyes which
captures the images and converts to signals that can be interpreted by
Chapter Two: Theoretical Principles
16
the brain and the visual pathways, that process and transmit this
information along the brain [37].
In recent years visual perception has increased in importance in computer
graphics, predominantly due to the demand for realistic computer generated
images [38, 39]. The goal of perceptually-based rendering is to produce imagery
that evokes the same responses as an observer would have when viewing a real-
world equivalent. To this end, work has been carried out on exploiting the
behavior of the HVS. Psychophysical experiments can be used to determine
responses such as sensitivity to a stimulus. In the field of computer graphics, this
information can then be used to design systems that are finely attuned to the
perceptual attributes of the visual system. To make an assessment of the effects
of reflected ambient light on the perception of electronically displayed images, it
is necessary to understand several perceptual phenomena that may play a part in
the process. The attributes of the HVS relevant are detailed below.
2.3.1 The human eye
HVS receives and processes electromagnetic energy in the form of light
waves. This starts with the path of light through the pupil (figure 2.5), which
changes in size to control the amount of light reaching the back of the eye. Light
then passes through the lens, which provides focusing adjustments, before
reaching the photoreceptors in the retina at the back of the eye. These receptors
in the retina consist of about 120 million rods and 8 million cones [40].
Figure 2.5: A schematic section through the human eye [41].
Chapter Two: Theoretical Principles
17
Rods are highly sensitive to light and provide low intensity vision in low light
levels, but they cannot detect color. They are located primarily in the periphery
of the visual field. In contrast to this, high-acuity color vision is provided
through three types of cones: L, which are sensitive to long wavelengths; M,
which are sensitive to medium wavelengths; and S, which are short wavelength
sensitive. Finally, the photo pigments in the rods and cones transform this light
into electrical impulses that are passed to neuronal cells and transmitted to the
brain via the optic nerve [41].
2.3.2 Visual sensitivity
The way in which can be perceive images depends on the amount of light
available. In dark scenes our visual acuity (the ability to resolve spatial detail) is
low and colors cannot be distinguished. Daylight (cone) vision is called
‘photopic vision’ and night (rod) vision ‘scotopicvision’, and between there is a
range of ‘mesopic vision’ where both cones and rods are active as shown in
figure (2.6) a luminance of white light above about 3 cd/mP
2P regarded as
photopic, and a luminance below 0.001 cd/mP
2P is scoptopic, while the mesopic
range is from 0.001 to 3 cd/mP
2P [40, 41].
Figure 2.6: The range of luminance in the natural environment
and associated visual parameters [41].
Chapter Two: Theoretical Principles
18
2.3.3 Contrast
The term contrast generally refers to the intensity difference between given
light and dark values. If the difference is great then the contrast is said to be
high; if small, then the contrast is low. Contrast can be computed in several
ways but one of the most common ways is the Michelson formula. The
Michelson formula is used to compute the contrast of a periodic pattern, and is
defined as [7]:
𝐶𝐶𝑤𝑤 = 𝐿𝐿𝑚𝑚𝑎𝑎𝑚𝑚 − 𝐿𝐿𝑚𝑚𝑖𝑖𝑚𝑚𝐿𝐿𝑚𝑚𝑎𝑎𝑚𝑚 +𝐿𝐿𝑚𝑚𝑖𝑖𝑚𝑚
(2.5)
Where 𝐿𝐿𝑚𝑚𝑎𝑎𝑚𝑚 and 𝐿𝐿𝑚𝑚𝑖𝑖𝑚𝑚 refer respectively to the maximum and minimum
luminance values in the pattern.
2.3.4 Thresholds
It is easily demonstrated that in a brightly-lit room the addition of a single
candle is not obvious, but when the room is dark, lighting a candle makes an
immediate impression. Similarly, a whisper is sufficient to be heard in a quiet
environment, whereas a shout is necessary in noisy conditions. In 1834 the
German physiologist, Weber observed this principle1, defining Weber’s Law
[40]: the ratio of the increment threshold to the background intensity is a
constant, denoted the Weber fraction that is given by [40]:
𝑘𝑘𝑤𝑤 = ∆𝐼𝐼𝑖𝑖𝐼𝐼𝑖𝑖
(2.6)
Here 𝐼𝐼𝑖𝑖 is the stimulus intensity (for example, a given luminance value), ∆𝐼𝐼𝑖𝑖 is
the increment or decrement in intensity needed for an observer to notice a
difference in the initial intensity, and 𝑘𝑘𝑤𝑤 is the value of the constant ratio.
2.3.5 The Contrast Sensitivity Function
The ability to perceive a just noticeable difference is known as contrast
sensitivity. In 1968 Campbell and Robson presented a theory of perception
showing that contrast sensitivity varies according to spatial frequency [41].
Spatial frequency indicates the number of gratings (pairs of bars, one black and
Chapter Two: Theoretical Principles
19
one white, also known as a cycle) which form a retinal image at a given distance
[40]. They measured this variation through the use of a compound sinusoidal
grating stimulus, as shown in figure (2.7). The use of gratings of different spatial
frequencies (i.e. with different numbers of cycles per degree of angle of vision)
means that contrast sensitivity can be measured at each spatial frequency. This
provides a curve that describes the threshold contrast needed to detect a given
spatial frequency, and this curve is known as the contrast sensitivity function
(CSF), which is shown in figure (2.7).
2.3.6 Adaptation
The HVS adjusts to the stimuli that are presented to it, resulting in changes
in sensitivity known as adaptation. This process enables the visual system to
respond to large variations in luminance, allowing it to adjust to the prevailing
light level. The rods in the eye are around ten times as sensitive as cones, and so
provide maximum sensitivity at low light levels [43].Visual adaptation from
light to dark is known as dark adaptation, and can last for tens of minutes; for
example, the length of time it takes the eye to adapt at night when the light is
Figure 2.7: The Campbell-Robson sensitivity chart (left, from [42]). The spatial
frequency increases logarithmically from left to right; the contrast varies
logarithmically from bottom to top. The resulting curve of the threshold determines an
individual’s contrast sensitivity function (right, from [40]).
Chapter Two: Theoretical Principles
20
switched off. Conversely, light adaptation, from dark to light, can take only
seconds, such as leaving a dimly lit room and stepping into bright sunlight. This
change in sensitivity is brought about through physiological processes. In high
luminance levels the photopigment in the eye is bleached, causing a loss of
sensitivity in the photoreceptors. The photoreceptors regain their sensitivity
gradually, accounting for the temporal aspects of adaptation. Additionally,
though less significantly, the amount of light entering the pupil changes [44].
2.3.7 Brightness perception
While luminance intensity can be measured on a physical scale
(photometric and radiometric), the term brightness actually denotes a perceptual
variable, which refers to a perceived level of illumination, such as the amount of
light an area appears to emit [40]. In addition, the term lightness usually refers o
the perceived reflectance of a surface. Brightness can be estimated for unrelated
stimuli (visual stimuli presented in isolation) and related stimuli (visual stimuli
presented alongside other visual stimuli) [45].The relationship between
luminance intensity and perceived brightness is non-linear and can be described
by a power law function as:
𝑆𝑆 = 𝑘𝑘𝐼𝐼𝑖𝑖𝑎𝑎 (2.7)
Known as the Stevens’ Power Law [46], where 𝑆𝑆 is the magnitude of the
sensation, 𝑘𝑘 is a scale constant, and 𝐼𝐼𝑖𝑖 is the intensity of the physical stimulus
raised to a power 𝑎𝑎. The exponent for brightness is experimentally determined to
be (0.33).
2.3.8 Lightness and color constancy
The ability to judge a surface’s reflectance properties despite any changes
in illumination is known as color constancy. Lightness constancy is the term
used to describe the phenomena whereby a surface appears to look the same
regardless of any differences in the illumination [47]. For example, white paper
with black text maintains its appearance when viewed indoors in a dark
Chapter Two: Theoretical Principles
21
environment or outdoors in bright sunlight, even if the black ink on a page
viewed outdoors actually reflects more light than the white paper viewed
indoors. Chromatic color constancy extends this to color: a plant seems as green
when it’s outside in the sun as it does if it’s taken indoors under artificial light.
2.4 Color Space Color is an important feature of many applications and processes, including
our everyday life. Color can be defined in several ways. When we look at an
object, we can usually straightaway tell which hue it has, i.e. what color “class”
it belongs to, whether it is red, green, yellow, blue and so on. We can also
distinguish between the color’s brightness, i.e. if the color is light or dark. With
these different features in mind, we need a space to describe them exactly and
therefore be able to differentiate between single colors. In 1931 the CIE
proposed the XYZ space which contains all colors human beings can see and is
based on three imaginary primary colors. The HSV, CIELAB and CIELUV or
the YIQ spaces are subsets of the CIE XYZ space and therefore represent only a
fraction of all possible colors. Moreover, most color spaces in this section are
not visually uniform .That is, distances in the color space do not reflect
perceived distances between two colors.
2.4.1 CIE chromaticity system
In 1931, the CIE defined three standard primary colors to be combined to
produce all possible perceivable colors. The three standard primaries of the 1931
CIE, called X, Y, and Z, are imaginary colors[48], figure (2.8) shows
mathematically with positive color matching functions, whose values are
specified the amount of each primary needed to describe any spectral color at 5
nm intervals. The CIE primaries provide an international standard definition for
all colors, and it eliminates negative value color matching and other problems
associated with selecting a set of real primaries [49]. Tristimulus values X, Y,
Chapter Two: Theoretical Principles
22
and Z are computed for a primary light source with power spectrum 𝐿𝐿(𝜆𝜆) from
the color-matching functions 𝑚𝑚, 𝑦𝑦, and 𝑧𝑧 as follows[50]:
𝑋𝑋 = 𝑘𝑘𝑚𝑚 ∫ 𝐿𝐿(𝜆𝜆) 𝑚𝑚(𝜆𝜆)𝑑𝑑𝜆𝜆𝜆𝜆 (2.8)
𝑌𝑌 = 𝑘𝑘𝑚𝑚 ∫ 𝐿𝐿(𝜆𝜆) 𝑦𝑦(𝜆𝜆)𝑑𝑑𝜆𝜆𝜆𝜆 (2.9)
𝑍𝑍 = 𝑘𝑘𝑚𝑚 ∫ 𝐿𝐿(𝜆𝜆) 𝑧𝑧(𝜆𝜆)𝑑𝑑𝜆𝜆𝜆𝜆 (2.10)
where 𝑘𝑘𝑚𝑚 = 683.002 lm/w, If tristimulus values X, Y, and Z are to be calculated
for a reflecting object, then the following equations are to be used[50]:
𝑋𝑋 = 𝑘𝑘𝑚𝑚 ∫ 𝑅𝑅(𝜆𝜆)𝐿𝐿(𝜆𝜆) 𝑚𝑚(𝜆𝜆)𝑑𝑑𝜆𝜆𝜆𝜆 (2.11)
𝑌𝑌 = 𝑘𝑘𝑚𝑚 ∫ 𝑅𝑅(𝜆𝜆)𝐿𝐿(𝜆𝜆) 𝑦𝑦(𝜆𝜆)𝑑𝑑𝜆𝜆𝜆𝜆 (2.12)
𝑍𝑍 = 𝑘𝑘𝑚𝑚 ∫ 𝑅𝑅(𝜆𝜆)𝐿𝐿(𝜆𝜆) 𝑧𝑧(𝜆𝜆)𝑑𝑑𝜆𝜆𝜆𝜆 (2.13)
Here R(λ) denotes the reflectance of the object. In this case, the constant 𝑘𝑘𝑚𝑚 is
set to [33]:
𝑘𝑘𝑚𝑚 = 100
∫ 𝐿𝐿(𝜆𝜆) 𝑦𝑦(𝜆𝜆)𝑑𝑑𝜆𝜆𝜆𝜆
(2.14)
Figure 2.8: CIE 1931 standard observe and chromaticity diagram [50].
Chapter Two: Theoretical Principles
23
If we normalize these tristimulus values, we get the so-called chromaticity
coordinates [33]:
𝑚𝑚 = 𝑋𝑋𝑋𝑋+𝑌𝑌+𝑍𝑍
, 𝑦𝑦 = 𝑌𝑌𝑋𝑋+𝑌𝑌+𝑍𝑍
, 𝑧𝑧 = 𝑍𝑍𝑋𝑋+𝑌𝑌+𝑍𝑍
(2.15)
By plotting (x) and (y) for all visible colors, a horseshoe-shaped diagram can
be drawn which is called the CIE chromaticity diagram. The interior and
boundary of the diagram represent all visible chromaticity values. In figure (2.8)
the boundary of the diagram represents the 100 percent pure colors of the
spectrum. The line joining the red and violet spectral points called the purple
line, is not part of the spectrum. The center point of the diagram represents a
standard white light, which approximates sunlight.
The forward and inverse matrix conversion from RGB to XYZ is expressed
mathematically as follows [33]:
𝑀𝑀𝑅𝑅𝑅𝑅𝑅𝑅 𝑡𝑡𝑐𝑐 𝑋𝑋𝑌𝑌𝑍𝑍 = �0.412 0.358 0.1800.213 0.715 0.0720.019 0.119 0.950
� (2.16)
𝑀𝑀 𝑋𝑋𝑌𝑌𝑍𝑍 𝑡𝑡𝑐𝑐𝑅𝑅𝑅𝑅𝑅𝑅 = �3.240 1.537 0.498−0.969 1.876 0.0420.056 0.204 1.057
� (2.17)
Although CIE XYZ defines all humanly perceivable colors, it is not
perceptually uniform. The distance between any two points in the space does not
determent the relative closeness of those colors. However, the CIE XYZ is
fundamental to colorimetric.
2.4.2 RGB color space
The RGB space is the most frequently used color space for image
processing. Since color cameras, scanners and displays are most often provided
with direct RGB signal input or output, this color space is the basic one, which
is, if necessary, transformed into other color spaces [51]. The RGB color model
is made of three additive primaries Red, Green, and Blue. It is the system used
Chapter Two: Theoretical Principles
24
in almost all color in the Cathode Ray Tube (CRT) monitors, and is device-
dependent (e.g. the actual color displayed depends on what monitor you have,
and what its settings are). It is called additive, because the three different
primaries are added together to produce the desired color. The color model is
shown as a cartesian cube, with usually Red being the x- axis, Green being the
y-axis, and Blue being the z-axis, as shown in figure (2.9) [52]. Each color,
which is described by its RGB components, is represented by a point and can be
found either on the surface or inside the cube.
All grey colors are placed on the main diagonal of this cube from black
(R=G=B=0) to white (R=G=B=max), The colors with a P are the primary
colors. The dashed line indicates where to find the grays, going from (0, 0, 0) to
(max , max, max) [33], where the max value equal 255 or 1.
2.4.3 YIQ color model
In the development of the NTSC television system used in the United
States, a color coordinate system with the coordinates Y, I, and Q is defined for
transmission purposes. To transmit a color signal efficiently, the R,G, and B
signal is more conveniently coded from a linear transformation. The luminance.
Signal is coded in the Y-component. The additional portions I and Q contain the
entire chromaticity information that is also denoted as chrominance signal in
Figure 2.9: RGB color space [52].
Chapter Two: Theoretical Principles
25
television technology [33].
(I) component containing orange-cyan hue information, and (Q) containing
green-magenta hue information. Transform color image from basic RGB color
space to YIQ color space by preformed by [51]:
𝑀𝑀𝑅𝑅𝑅𝑅𝑅𝑅 𝑡𝑡𝑐𝑐 𝑌𝑌𝐼𝐼𝑌𝑌 = � 0.299 0.587 0.1140.596 − 0.270 − 0.3220.211 − 0.253 0.312
� (2.18)
While the inverse transformation is given by:
𝑀𝑀𝑌𝑌𝐼𝐼𝑌𝑌 𝑡𝑡𝑐𝑐 𝑅𝑅𝑅𝑅𝑅𝑅 = �1 0.956 0.6211 − 0.272 − 0.6471 − 1.060 1.703
� (2.19)
There are two peculiarities with the YIQ color model, the first is that this
system is more sensitive to changes in luminance than to changes in
chromaticity; the second is that color gamut is quite small; it can be specified
adequately with one rather than two color dimensions. These properties are very
convenient for the transfer of TV signals [53].
2.4.4 HSV Color Model The three components in HSV color model are hue (H), saturation (S) and
value (V). Hue is an attribute associated with the dominant wavelength in a
mixture of light waves [54]. For example, a light wave with central tendency of
565 to 590 nm will be perceived as “yellow” by human observer. In HSV color
model, hue represents the dominant color as observed by the human eye and
measured in degree from 0° to 360°. Saturation measures how vivid or pure a
color is and the purity refers the amount of “white” color mixed with a hue [54].
A highly saturated color implies a pure color while no saturation makes the hue
appear grey. The degree of saturation is inversely proportional to the amount of
white light added and white color has zero saturation. Value represents
brightness of a color. While hue and saturation defines chromaticity, value
represents the achromatic notion of its intensity. Pure achromatic colors range
Chapter Two: Theoretical Principles
26
from black to white with all the possible gray colors in between. HSV color
space can be represented in various ways [50,51,54]. One typical representation
is using a hexagonal disk model The saturation S and hue H components specify
a point inside the hexagonal disk, Saturation for a given level of V is defined as
the relative length of the vector that points to the given color to the length of the
vector that points to the corresponding color on the border or the hexagonal disk
[50]. This results in a set of loci of constant S as shown in figure (2.10).
The transformation from RGB color space to HSV color space is given by [50]:
𝑉𝑉 = 𝑚𝑚𝑎𝑎𝑚𝑚{𝑅𝑅,𝑅𝑅,𝑅𝑅} (2.20)
𝑆𝑆 = 𝑚𝑚𝑎𝑎𝑚𝑚 −𝑚𝑚𝑖𝑖𝑚𝑚𝑚𝑚𝑎𝑎𝑚𝑚
(2.21)
𝐻𝐻 =
⎩⎪⎨
⎪⎧
𝑅𝑅−𝑅𝑅6(𝑚𝑚𝑎𝑎𝑚𝑚 −𝑚𝑚𝑖𝑖𝑚𝑚 )
16
(2 + 𝑅𝑅−𝑅𝑅𝑚𝑚𝑎𝑎𝑚𝑚 −𝑚𝑚𝑖𝑖𝑚𝑚
)16
(4 + 𝑅𝑅−𝑅𝑅𝑚𝑚𝑎𝑎𝑚𝑚 −𝑚𝑚𝑖𝑖𝑚𝑚
)
� (2.22)
where 𝑚𝑚𝑎𝑎𝑚𝑚 = 𝑚𝑚𝑎𝑎𝑚𝑚{𝑅𝑅,𝑅𝑅,𝑅𝑅} and 𝑚𝑚𝑖𝑖𝑚𝑚 = 𝑚𝑚𝑖𝑖𝑚𝑚{𝑅𝑅,𝑅𝑅,𝑅𝑅}. All three components 𝑉𝑉,
S, and H are in the range [0, 1]. The transformation from HSV back to RGB is
given by [50]:
[𝑅𝑅,𝑅𝑅,𝑅𝑅] = [𝑉𝑉,𝑉𝑉,𝑉𝑉] 𝑖𝑖𝑖𝑖 𝑆𝑆 = 0 (2.23)
Figure 2.10: HSV
Color Space .
Chapter Two: Theoretical Principles
27
If the saturation is not zero, then the RGB components are given:
[𝑅𝑅,𝑅𝑅,𝑅𝑅] =
⎩⎪⎪⎨
⎪⎪⎧
[𝑉𝑉,𝐾𝐾,𝑀𝑀] 𝑖𝑖𝑖𝑖 0 ≤ 𝐻𝐻 < 1/6 [𝑁𝑁,𝑉𝑉,𝑀𝑀] 𝑖𝑖𝑖𝑖 1/6 ≤ 𝐻𝐻 < 2/6 [𝑀𝑀,𝑉𝑉,𝐾𝐾] 𝑖𝑖𝑖𝑖 2/6 ≤ 𝐻𝐻 < 3/6 [𝑀𝑀,𝑁𝑁,𝑉𝑉] 𝑖𝑖𝑖𝑖 3/6 ≤ 𝐻𝐻 < 4/6 [𝐾𝐾,𝑀𝑀,𝑉𝑉] 𝑖𝑖𝑖𝑖 4/6 ≤ 𝐻𝐻 < 5/6[𝑉𝑉,𝑀𝑀,𝑁𝑁] 𝑖𝑖𝑖𝑖 5/6 ≤ 𝐻𝐻 < 1
� (2.24)
where 𝑀𝑀, 𝑁𝑁, and 𝐾𝐾 are defined as:
𝑀𝑀 = 𝑉𝑉 (1 − 𝑆𝑆) (2.25) 𝑁𝑁 = 𝑉𝑉 (1 − 𝑆𝑆𝑆𝑆) (2.26)
𝐾𝐾 = 𝑉𝑉 (1 − 𝑆𝑆(1 − 𝑆𝑆)) (2.27)
And 𝑆𝑆 = 6𝐻𝐻 – 𝑉𝑉 (2.28)
2.4.5 CIELAB COLOR SPACE
The CIELAB color coordinate system is developed to give a simple
measure of color in agreement with the Munsell color system. The CIELAB
space has been designed to be a perceptually uniform space. A system is
perceptually uniform if a small perturbation to a component value is
approximately equally perceptible across the range of that value [33].
In CIELAB, the L-axis is known as the lightness and extends from 0 (black) to
100 (white). The other two coordinates A and B represent redness-greenness and
yellowness-blueness, respectively and samples for which A=B=0 are
achromatic. Therefore, the L-axis represents the achromatic scale of grays from
black to white. The three coordinates L, A and B are computed from the
tristimulus values X, Y, and Z as follows [50]:
𝐿𝐿 = �116( 𝑌𝑌
𝑌𝑌𝑚𝑚)
13 − 16 𝑖𝑖𝑖𝑖 𝑌𝑌
𝑌𝑌𝑚𝑚> 0.008856
116( 𝑌𝑌𝑌𝑌𝑚𝑚
)13 𝑖𝑖𝑖𝑖 𝑌𝑌
𝑌𝑌𝑚𝑚≤ 0.008856
� (2.29)
Chapter Two: Theoretical Principles
28
𝐴𝐴 = 500(𝑖𝑖 � 𝑋𝑋𝑋𝑋𝑚𝑚� − 𝑖𝑖 �𝑌𝑌
𝑌𝑌𝑚𝑚�) (2.30)
𝑅𝑅 = 200(𝑖𝑖 � 𝑌𝑌𝑌𝑌𝑚𝑚� − 𝑖𝑖 �𝑍𝑍
𝑍𝑍𝑚𝑚�) (2.31)
where Xn, Yn, and Zn describe a specified white object color stimulus and the
function f is defined as[50]:
𝑖𝑖(𝑚𝑚) = �𝑋𝑋
13 𝑖𝑖𝑖𝑖 𝑋𝑋 > 0.008856
7.787𝑋𝑋 + 16116
𝑖𝑖𝑖𝑖 𝑋𝑋 ≤ 0.008856� (2.32)
The B coordinate represents the yellowness–blueness. The coordinates A and B
have a range of approximately [−100, 100] [33].
And chroma , hue angle given by[49]:
𝐶𝐶𝐴𝐴𝑅𝑅 = √𝐴𝐴2 + 𝑅𝑅2 (2.33)
𝐻𝐻𝐴𝐴𝑅𝑅 = 𝑡𝑡𝑎𝑎𝑚𝑚−1 𝑅𝑅𝐴𝐴 (2.34)
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
29
3.1 Introduction This chapter includes image Analysis using image equality assessment based
on changing in the lightness and contrast, many methods used to predict quality
of the color image, some depending on the optimal reference is called full
reference image quality, and the other do not dependent on optimal reference is
called no reference image quality. In this study we focus on the second types
due to the changing in lightness and contrast where there is not an optimal
image. And, in this chapter we were introduced the color image enhancement
algorithms, these algorithm enhance the lightness and contrast for the color
images.
3.2 Image Quality Measurement Measurement of image quality is very important for many image-
processing algorithms, such as acquisition, compression, restoration,
enhancement and other applications. Image quality assessment is a very
important activity for many image applications. The image quality metrics can
be broadly classified into two categories, subjective and objective. A large
numbers of objective image quality metrics have been developed during the last
decade. Objective metrics can be divided in three categories: Full Reference,
Reduced Reference and No Reference. The best way to assess the quality of an
image is to ask observers to look at it as the HVS is the end-receiver in most
processing environments. However, this approach is tiresome and it requires
long time Moreover, it needs a normalized environment ensuring the best
conditions for the targeted application, thus we used objective measurement.
3.2.1 Subjective Quality Measurement
Essentially, image quality is always an outcome of human sensation. HVS
is the final decisions about quality based on their own visual preferences that,
naturally, are not only affected by the psychophysical aspects of the observer,
but also the fidelity of the image and the observation situation. For evaluating
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
30
image quality, testing with human observers, i.e. subjective evaluation, is often
considered the most reliable way to estimate the quality of images[55,56].
The mean opinion score (MOS) is often regarded as the most reliable image
quality measure, but since it requires numerous human observations and a
specific test arrangement, MOS is also slow and expensive method in real world
situations [56].
3.2.2 Objective Quality Measurement
Objective image quality measures play important roles in various
applications in the image processing and they are numerical measures. A good
objective measure reflects the distortion on image due to blurring, noise,
compression, sensor inadequacy and any source can be distorted the image.
Some time objective image quality assessment models typically require the
access to a reference image that is assumed to have perfect quality. Objective
assessment relies on computational models that can predict the image quality
observations of humans. The accurate objective image quality model predicts
the image quality sensation of an average human observer in other words, strong
correlations to subjective observations are essential when defining a good
objective quality model [55]. Because image quality is strongly based on
subjective observations, traditional objective models such as the mean-squared-
error (MSE) rarely work accurately on a quality context [57].
Depending on how much prior information is available and on how a perfect
candidate image should look like, objective image quality algorithms can be
classified as following:
a. Full Reference image quality
Full reference image quality assessment mostly interprets image quality as
accuracy or similarity with a “reference” or “perfect” image in some perceptual
space. The image quality assessment algorithms attempt to achieve consistency
in quality prediction by modeling salient physiological and psycho visual
features of the HVS, or by signal fidelity measures. Full Reference image
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
31
quality methods approach the image quality assessment problem as an
information fidelity problem [55, 58]. The reference signal is typically processed
to yield a distorted visual data, which can be compared to the reference using
full reference methods. Typically, this comparison involves measuring the
distance between the two signals in a perceptually meaningful way. This can be
achieved by studying, characterizing and deriving the perceptual impact of the
distorted signal to human viewers by means of subjective experiments. The full
reference metric is convenient for image coding scheme comparison [59].
b. Reduced Reference image quality
Reduced-reference measures occur between full-reference and no-reference
measures .The Reduced-reference image quality measures prepare to predict the
visual quality of distorted images with partial information about the reference
images. Reduced-reference approaches are clearly introduced in video
applications [60].
c. No reference image quality
No reference image quality refers to the problem of predicting the visual
quality of image without any reference to an original optimal quality image. This
assessment is the most difficult problem in the field of image objective analysis
[56], since many unquantifiable factors play a role in human perceptions of
quality, such as aesthetics, cognitive relevance, learning, context etc [60]. No
reference image quality is useful to many still image applications as assessment
equality of high resolution image, JPGE image compressed [6] moreover, this
objective method can measure image equality depending on verity of lightness
and contrast.
3.3 Image Quality assessment The image quality assessment is important process to determining the level
of enhancement, in this section we present many algorithms to assess the quality
of the color image based in lightness change such as The Structural Similarity
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
32
Index, the entropy of the first derivative image, Mean of locally (µ ,σ ) model
and color fullness metrics, moreover we suggest new algorithm called quality
factor assessment.
3.3.1 The Structural Similarity Index (SSIM)
Mean squared error (MSE) and Peak Signal to Noise Ratio (PSNR) are the
most common measures of full-reference image quality but, in formulas include
error difference a large errors do not always result in large structural distortions
.this can be interpreted as follows [62]:
If we will use the Minkowski error metric as an example , in the spatial
domain, the Minkowski metric between a reference image 𝑋𝑋𝑖𝑖 (assumed to
have perfect quality) and a distorted image 𝑌𝑌𝑖𝑖 is defined as:
𝐸𝐸𝑝𝑝 = �∑ |𝑋𝑋𝑖𝑖−𝑌𝑌𝑖𝑖|𝑝𝑝𝑁𝑁𝑖𝑖=1
�1/𝑝𝑝 (3.1)
Where 𝑋𝑋𝑖𝑖 and 𝑌𝑌𝑖𝑖 are the 𝑖𝑖_𝑡𝑡ℎ samples in images 𝑋𝑋𝑖𝑖 and 𝑌𝑌𝑖𝑖 ,
respectively, 𝑁𝑁 is the number of image samples, and 𝑝𝑝 refers to the degree
of power. Figure (3.1) shows a two distorted images generated from the same
original image. The first distorted image is obtained by adding a constant
number to all signal samples, and the second is generated using the same
method except that the signs of the constant are randomly chosen to be positive
or negative. It can be easily shown that the Minkowski metrics between the
original image and both of the distorted images are exactly the same, no
matter what power 𝑝𝑝 is used. However, the visual quality of the two distorted
images is drastically different. To overcome the problem in the measurements
that depending on error deference Zhou Wang and Alan Bovik suggested in the
full image quality assessment using the Structural Similarity Index (SSIM) [63].
The basic SSIM algorithm requires that the two images being compared are
properly aligned and scaled so they can be compared point by point. The
computations are performed in a sliding 𝑁𝑁𝑁𝑁𝑁𝑁 (typically 11x11) gaussian-
weighted window.
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
33
The SSIM metric is based on the evaluation of three different measures, the
luminance, contrast, and structure comparison measures are computed as[20]:
1
221
),(),(),(),(2),(
CyxyxCyxyxyxl
YX
YX
+++
=µµµµ (3.2)
222
2
),(),(),(),(2),(
CyxyxCyxyxyxc
YX
YX
+++
=σσσσ (3.3)
3
3
),(),(),(),(
CyxyxCyxyxs
YX
XY
++
=σσ
σ (3.4)
Where X and Y correspond to two different images that we would like to
match, i.e. two different blocks in two separate images, xµ , 2xσ , and xyσ the mean
of 𝑋𝑋, the variance of 𝑋𝑋, and the covariance of 𝑋𝑋 and 𝑌𝑌 respectively where[20]:
𝜇𝜇(𝑁𝑁, 𝑦𝑦) = ∑ ∑ 𝑤𝑤(𝑝𝑝, 𝑞𝑞)𝑋𝑋(𝑁𝑁 + 𝑝𝑝,𝑦𝑦 + 𝑞𝑞)𝑄𝑄𝑞𝑞=−𝑄𝑄
𝑃𝑃𝑝𝑝=−𝑃𝑃 (3.5)
σ2(x, y) = ∑ ∑ w(p, q)[X(x + p, y + q)Qq=−Q
Pp=−P − μX(x, y)]2 (3.6)
Figure 3.1: Failure of the Minkowski metric for image quality prediction. A: original
image; B distorted image by adding a positive constant; C distorted image by adding the
same constant, but with random sign. Images B and C have the same Minkowski metric
with respect to image. A, but drastically different visual quality [9].
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
34
𝜎𝜎 .𝑋𝑋𝑌𝑌(𝑁𝑁,𝑦𝑦) = � � 𝑤𝑤(𝑝𝑝, 𝑞𝑞)[𝑋𝑋(𝑁𝑁 + 𝑝𝑝, 𝑦𝑦 + 𝑞𝑞)
𝑄𝑄
𝑞𝑞=−𝑄𝑄
𝑃𝑃
𝑝𝑝=−𝑃𝑃
− 𝜇𝜇𝑋𝑋(𝑁𝑁,𝑦𝑦)]
[ Y(x + p, y + q) − μY(x, y)] (3.7)
Where w(p, q) is a Gaussian weighing function such that:
∑ ∑ w(p, q) = 1Qq=−Q
Pp=−P (3.8)
And 1C , 2C , and 3C are constants given by ( )211 LKC = , ( )222 LKC = , and 2/23 CC =
. L is the dynamic range for the sample data, i.e. 𝐿𝐿 = 255 for 8 bit content and
𝐾𝐾1 << 1 and 𝐾𝐾2 << 1 are two scalar constants In this study we used 𝐾𝐾1=0.01
and 𝐾𝐾2=0.03 [20]. Given the above measures the structural similarity can be
computed as [20]:
[ ] [ ] [ ]),(),(),(),( yxsyxcyxlyxSSIM ⋅⋅= (3.9)
3.3.2 The Entropy of the First Derivative (EFD) Image
This method dependent on the first derivative of an image can be shown by
the following formula:
𝐼𝐼𝑑𝑑(𝑁𝑁, 𝑦𝑦) = 𝜕𝜕2𝐼𝐼(𝑁𝑁 ,𝑦𝑦)𝜕𝜕𝑁𝑁𝜕𝜕𝑦𝑦
(3.10)
Figure (3.2) show the first derivative of high lightness and low lightness
images. The entropy of the first derivative is defined as follows [18]:
𝐻𝐻(𝜒𝜒) = ∑ 𝑃𝑃(𝑁𝑁𝑘𝑘) log2( 1𝑃𝑃(𝑁𝑁𝑘𝑘)
𝑛𝑛𝑘𝑘=1 ) (3.11)
Where 𝜒𝜒 is a discrete random variable with possible outcomes xR1R, xR2R,... xRnR;
𝑃𝑃(𝑁𝑁𝑘𝑘)is the probability of the outcome 𝑁𝑁𝑘𝑘 . The outcome is understood as a gray
level in the lightness image, and its probability is calculated by:
𝑃𝑃(𝑁𝑁𝑘𝑘) = 𝑛𝑛𝑘𝑘𝑁𝑁𝑡𝑡
(3.12)
Where 𝑘𝑘 = 1 ,2 , . . .𝑛𝑛, 𝑛𝑛 is the total number of possible lightness in the image,
𝑁𝑁𝑡𝑡 is the total number of pixels, and nRkR is the number of pixels that have
lightness level 𝑁𝑁𝑘𝑘 . The higher entropy value denotes a better contrast in the
image.
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
35
3.3.3 The Mean of locally (µ ,σ ) model
In NASA Langley research center [12] concluded heuristics to measure
quality of the image, proposed the idea that good visual representations seem to
be based upon some combinations of high regional visual lightness and contrast.
To compute the regional parameters, we divided the image into non overlapping
blocks that are 50×50 pixels. For each block, the mean (𝐼𝐼) and a standard
deviation (𝜎𝜎) are computed, and then taking the mean of them 𝐼𝐼 ̅ and 𝜎𝜎� as shown
in figure (3.3). If the points tend to visual optimal region the image has higher
quality of lightness and contrast, whereas if 𝜎𝜎� (without𝐼𝐼)̅ is increased, it makes
image having insufficient lightness, but if 𝐼𝐼 ̅ (without 𝜎𝜎� ) is increased it makes
insufficient contrast in the image.
Figure 3.2: images and its first derivative in different lightness levels.
(a) High lightness. (b) First derivative of (a). (c) Low lightness. (d) First derivative of (c).
σFigure 3.3: Image quality description in the mean of locally
mean and standard deviation of image model [12].
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
36
3.3.4 Color Fullness Metrics (CM)
Colorfulness or called Chromaticness is the attribute of a visual sensation
according to which the perceived color of an area appears to be more or less
chromatic [13] for evaluating the colorfulness of a digital image is to examine
the distribution of pixels in CIELAB color space. Then, the image colorfulness
can be summarized as a linear combination of different characteristic quantities
of the said distribution. Based on the testing of numerous colorfulness methods
with diverse weighting coefficients, two metrics for images in CIELAB color
space are introduced for objective colorfulness calculations one of them [13]:
𝑀𝑀 = 𝜎𝜎𝐴𝐴𝐴𝐴 + 0.94𝜇𝜇𝑐𝑐 (3.13)
And
𝜎𝜎𝐴𝐴𝐴𝐴 = �(𝜎𝜎𝐴𝐴 )2 + (𝜎𝜎𝐴𝐴)2 (3.14)
Where 𝜎𝜎𝐴𝐴𝐴𝐴 is the trigometric length of the standard deviation in A and B
components and 𝜇𝜇𝑐𝑐 is the mean of chroma component.
3.4 Contrast and Lightness Enhancement Algorithms Many algorithms are suggested to enhance color images from any type of
distortion (such as noise, color shift, inverse transform for any operation and
lightness change) ,in this section we introduce many enhancement methods such
as Histogram equalization, Multi scale retinex algorithm and AINDANE
algorithm these algorithm used to enhancement lightness and contrast in the
color image. 3.4.1 Histogram Equalization (HE)
Histogram equalization and its variations have traditionally been used to
correct for uniform lighting and exposure problems. This technique is based on
the idea of remapping the histogram of the scene to a histogram that has a near-
uniform probability density function. This results in reassigning dark regions to
brighter values and bright regions to darker values. Histogram equalization
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
37
works well for scenes that have unimodal or weakly bi-modal histograms (i.e.
very dark, or very bright), but not so well for those images with strongly bi-
modal histograms (i.e. scenes that contain very dark and very bright regions)[9].
HE the global technique that works well for a wide variety of images is
histogram equalization If lightness levels are continuous quantities normalized
to the range (0, 1), pRrR(r) which denote the probability density function (PDF) of
the lightness levels in a given image, where the subscript is use for
differentiating between the PDFs of the input and output images. Suppose that
we perform the following transformation on the input levels to obtain output
(processed) intensity levels [1],
∫==r
dwwrprTs0
)()( (3.15)
Where w is a dummy variable of integration, that the probability density
function of the output levels is uniform, that is[1]:
≤≤
=else
sforssP0
101)( (3.16)
When dealing with discrete quantities we work with histograms and call the
preceding technique histogram equalization, where [4]:
∑=
=∑=
=k
ojjn
jrk
j rpksN
)(0
Lk ,...2,1,0= (3.17)
L =255 for lightness band with 8 bit/pixel), sRkR corresponding normalized
intensity level of the output image and nRjR being the number of pixel with
intensity level j and n is the total number. The eq (3.17) is represent the
cumulative probability density function (CPDF) . rRjR is normalized intensity level
of the input image corresponding to the (un –normalized) intensity level this
algorithm summarized by using following steps:
1. Input color image C(n,m,i), i=1,2,3 ( red ,green, blue) components.
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
38
2. Normalize each component rRjR(i)= C(x,y,i)/ 255 and calculated frequency of occurrence each gradual level nRjR(i) , where j=0,1,..255 .
3. Compute histogram from P(rRjR(i))=nRjR(i) /N , where N being the size of image. 4. Calculate cumulative histogram by :
∑=
=k
j N
ijniks
0
)()( where k=0,1,..255 .
5. Replace each normalized component rRj (i)Rby value of sRkR(i) we get the output image .
3.4.2 Multi Scale Retinex Algorithm
The multiscale retinex (MSR) is explained from single-scale retinex (SSR)
we have [2,8]:
)],(),,(log[)],(log[),,( yxiIcyxFyxiIcyxiR ⊗−= (3.18)
Where ),,( cyxiR the output of channel i ( i ∈ R,G,B) at position x,y , c is
the Gaussian shaped surrounding space constant , ),( yxiI is the image value for
channel i and symbol ⊗ denoted convolution . ),,( cyxF Gaussian surrounds
function that is calculated by[2]:
)2)22(exp()(),,(
cyxkcyxF −−= (3.19)
k is determined by[8]:
1),,( =∫∫ dydxcyxF (3.20)
The MSR output is then simply a weighted sum of the outputs of several
different SSR output where[2,8]:
∑=
=N
n ncyxiRnWcwyxMSRR1
),,(),,,( (3.21)
Where N is the number of scales, ),,( ncyxiR the i'th component of the n’th
scale, ),,,( cWyxMSRR the i'th spectral component of the MSR output and nW
the weight associated with the n’th scale. And we insist that ( 1=∑ nW ). The
result of the above processing will have both negative and positive RGB values,
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
39
and the histogram will typically have large tails. Thus a final gain-offset is
applied as mentioned in [8] and discussed in more detail below. This processing
can cause image colors to go towards gray, and thus an additional processing
step is proposed in [2]:
),,('.' cyxiIMSRRR = (3.22)
Where I' given by
]3
1),(
),(1log[),,('
∑=
+=
iyxiI
yxiIaayxiI (3.23)
Where we have taken the liberty to use log(1+x) in place of log(x) to ensure a
positive result. In [2] a value of 125 is suggested for (a) second. And the final
step is gain-offset by 0.35 and 056 respectively [11]. In this work we used
(wR1R=wR2R=wR3R=1/3 ) and (cR1R=250,cR2R=120,cR3R=80) [2] .
This algorithm done by using following steps:
1. Input color image IRiR(x,y) , i= r,g,b . 2. Calculate Gaussian surrounds function ))(exp()(),,( 2
22
nn c
yxkcyxF −−= ,
where k is normalization constant, cRnR, n=3, {cR1R=250, cR2R=120, cR3R=80}. 3. Compute SSR from )],(),,(log[)],(log[),,( yxIcyxFyxIcyxR inii ⊗−= .
4. Compute MSR from ∑=
=N
nninMSR cyxRWcwyxR
1),,(),,,( , N=3
{wR1R=wR2R=wR3R=1/3}.
5. Calculate MSR with color restoration by: ]),(
),(1log[),,,(' 3
1∑=
+=
ii
ii
yxI
yxIabbayxI ,
b=100, a=125. 6. Output image is gotten form gain offset by IRpiR(x,y)=0.35( ),,,(' bayxIi +0.56).
3.4.2 AINDANE algorithm
Adaptive Integrated Neighborhood Dependent Approach for Nonlinear
Enhancement of Color Images (AINDANE) [4] is an algorithm to improve the
visual quality of digital images captured under extremely low or uniform
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
40
lightening conditions. it is composed of three main parts: adaptive Luminance
enhancement, contrast enhancement and color restoration, this algorithm can be
described as follows[4]:
a. Adaptive luminance enhancement: is consisting of two steps, the first step
is luminance estimation to obtain by conversion of the luminance information by
using National Television Standards Committee (NTSC) color space. Intensity
values of RGB image can be obtained using eq. (2.18) and the normalized
intensity is:
255/),(),( yxIyxnI = (3.24)
The image information according to human vision behavior can be
simplified and formulated as [1]:
),(),(),( yxRyxLyxI = (3.25)
Where ),( yxR is the reflectance and ),( yxL is the illumination at each position
),( yx , the luminance L is assumed to be contained in the low frequency
component of the image while the reflectance R, mainly represents the high
frequency components of the image. For estimation of illumination, the result
of Gaussian low-pass filter applied on the intensity image is used. In spatial
domain, this process is a 2D discrete convolution with a Gaussian kernel which
can be expressed as:
),,(),(),(),( cyxFyxIyxLyxcI ⊗== (3.26)
The IRcR is image convolution.
The second step is called adaptive Dynamic range compression of
illuminance it can be applied using the transfer function nT by [4]:
2
25.0)1(24.0nInInI
nT +−+= (3.27)
This transformation can largely increase the luminance for the dark pixels,
figure (3.4) illustrate the relationship graphically.
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
41
b. contrast enhancement: is done by Center-surround contrast enhancement
using[4] :
),(),(255),( yxEyxnTyxR = (3.28)
pyxIyxcIyxE )),(),((),( = (3.29)
Where p constant can be manually adjusted by users to tune the contrast
enhancement process generally its value is ( 25.0 ≤≤ p ). It depends on the
global standard deviation of the input intensity image.
According to this method, if the center pixel’s intensity is higher than the
average intensity of surrounding pixels, the corresponding pixel on the intensity
enhanced image will be pulled up, otherwise it will be pulled down. In fact, this
process is an intensity transformation process. Considering the enhanced
intensity pixels are in the range [0 1] and the power of these pixels.
c. Color restoration: a linear color restoration process is applied, it is based on
the chromatic information of the original image it is applied to convert the
enhanced intensity image to RGB color image .The (𝑟𝑟′,𝑔𝑔′, 𝑏𝑏′) of the restored
color image are obtained by [9]:
Figure 3.4: Relationship between input lightness versus output lightness
in AINDANE algorithm.
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
42
bIRhbgI
RhgrIRhr === ',',' (3.30)
),(),( yxiRi iwyxR ∑= (3.31)
Where 𝑖𝑖 = 1,2,3, … represents different scales (cRiR) and wRiR is the weight factor
for each constant enhancement and (R1, R2, R3) are calculated from equation
(3.28). The scales used in this work are [4]: cR1R=5, cR2R= 20 and cR3R=240 and
wR1R=wR2R=wR3R=1/3.
Figure (3.5) show steps of AINDANE algorithm that is done by the following:
1. Input color image 𝐶𝐶(𝑁𝑁, 𝑦𝑦).
2. Transform color image 𝐶𝐶(𝑁𝑁,𝑦𝑦) from RGB space to YIQ space and estimated lightness component 𝑌𝑌(𝑁𝑁,𝑦𝑦).
Illumination Reflectance
Contrast enhancement
Input color image
Intensity image
Illumination
estimation and
reflectance
Adaptive dynamic
range compression
Illumination
enhancement
Color restoration
Output color image
Figure 3.5: The diagram of AINDANE algorithm.
⊗
Chapter Three: Color Image Analysis and Enhancement Based on Changing in Lightness and Contrast
43
3. Normalize the lightness component 𝐼𝐼(𝑁𝑁,𝑦𝑦) = 𝑌𝑌(𝑁𝑁,𝑦𝑦)/255 . 4. Calculate the Gaussian surrounds function ))(exp()(),,( 2
22
nn c
yxkcyxF −−= ,
where k is normalization constant, cRnR, n=3, {c1=5, c2=20, c3=240}. 5. Compute the convolution image from:
∑∑−
=
−
=
++=1
0
1
0),,(),(),(
M
m
N
nnc cynxmFnmIyxI
6. Calculate 2
25.0)1(24.0nInInI
nT+−+
=
7. Compute reflectance image from ),(),(255),( yxEn yxTyxR = ,
pc
yxIyxIyxE )),(),((),( = ,P=1
8. Output the image result from components ; bIRbg
IRgr
IRr === ','',' .
Chapter four: Lighting Systems and Suggested Algorithms of Image Quality Assessment and Enhancement
44
4.1 Introduction This chapter focuses on two parts: the first is the experimental part of the
lighting system belongs to the distribution of the lightness and features of
images. These images are captured under the LEDs and MH lighting systems.
The LEDs lighting system is used to lit images in many cases from low to
moderate lightness, however, the MH lightness system is used to lit images from
moderate to high lightness levels. Part two include Images analysis and
enhancement , the analysis of images has been done by using suggested method
of image quality assessment based on changing in lightness and contrast, this
method depending on edge region in the lightness component. And we suggest
two methods to enhance the color image, the first method depending on the
retinex algorithm and the other depending on histogram equalization. All
Algorithms and calculations have been done by using Matlab version 8, except
fitting calculations are done by table curve version 5.
4.2 Lighting Systems and Images Features All images in this study are taken by digital Sony camera model (DCR-
HC62E) and remote control device which is used so that the image will not be
disturbed. These images captured are of the (JPEG) format with size (640×480
pixel) and these images covered area about (52×40 cmP
2P) in the real word. The
control of lightness is done by using the LEDs system of lighting and MH
system as follows:
4.2.1. LEDs lighting system
Figure (4.1) shows the LEDs lighting system which consists of circuit
board with 98 chips LEDs arranged in circular forms (four circles in all with
the same center), the radii of these circles are (5,7,9 and 12 cm) . In the center
there is a circular aperture with radial (3 cm) that is used to fix the camera that
will capture the plane of the image perpendicular to the center of the camera
lens. Next to the board, there is a power supply with switches (89 small switches
Chapter four: Lighting Systems and Suggested Algorithms of Image Quality Assessment and Enhancement
45
for each one chip LEDs) inside this power supply lies a low voltage electrical
transformer. To control the illuminance by switching on the chips LEDs
gradually (one by one or more that). The illuminance is measured by using
luxmeter that is shown in figure (4.2).
4.2.2 MH lighting system
Figure (4.3.a) demonstrates the MH lighting system which consists of
projector that contains the MH lamp .The center of camera lens (CCL) is located
beside the projector to capture the image (image plane IP) in maximum angle
Figure 4.1: LEDs lighting system in two view (a) Forward (b) Backward.
(a) Forward view LEDs lighting system.
Camera Aperture
Power supply
White LEDs
(b) Backward view LEDs lighting system.
Camera fixed in
the aperture in
the center of
LEDs board.
Power supply
contains 89
switches.
Back of LEDs board.
Chapter four: Lighting Systems and Suggested Algorithms of Image Quality Assessment and Enhancement
46
about 30°between the lens an center of projector(CP) , (at distance 40cm
between the center of camera lens and image plane),this demonstrated in figure(
3.4.b). To control the illuminance by moving the projector faraway the scene,
this leads to decrease the angel between CP and CCL.
4.3 Adaptive method in the Objective Quality based on lightness
changing In this method the image quality has determined depending on edge
regions for lightness component in YIQ color space that is given by [8]:
Figure 4.2: digital luxmetre model( LX1330B) china.
Figure 4.3: MH lighting system in (a) and their geometric view at distance 40cm
between the center of camera lens(CCL) and image plane(IP) in (b).
Camera
Projector
MH lamp
(a)
(b)
30°
40cm CP
CCL
IP
Chapter four: Lighting Systems and Suggested Algorithms of Image Quality Assessment and Enhancement
47
𝑦𝑦 = 0.596𝑟𝑟 − .0270𝑔𝑔 + 0.322𝑏𝑏 (4.1)
And the edge image gets by using Sobel edge detection with two kernel that is
given [1]:
𝐺𝐺𝑥𝑥 = �−1 0 − 1−2 0 − 2−1 0 − 1
� (4.2)
𝐺𝐺𝑦𝑦 = �1 2 10 0 0−1 − 2 − 1
� (4.3)
So, edge image can be determined using:
𝐼𝐼𝑥𝑥 = 𝑦𝑦⊗𝐺𝐺𝑥𝑥 (4.4)
𝐼𝐼𝑦𝑦 = 𝑦𝑦⊗𝐺𝐺𝑦𝑦 (4.5)
𝐼𝐼𝐼𝐼 = max(𝐼𝐼𝑥𝑥 , 𝐼𝐼𝑦𝑦) (4.6)
Where 𝐺𝐺𝑥𝑥,𝐺𝐺𝑦𝑦 being horizontal and vertical of Sobel kernel, and 𝐼𝐼𝐼𝐼 is the
edge image. The edge determined in the image depending on threshold value
(𝑡𝑡) from (0 to 255) or (0 to 1) if the image is normalized. Let 𝑛𝑛𝑏𝑏 is the number
of black pixel and 𝑛𝑛𝑛𝑛 the number of white pixel the contrast factor given by:
𝐶𝐶𝐶𝐶(𝑡𝑡) = �𝑚𝑚𝑚𝑚𝑛𝑛 (𝑛𝑛𝑛𝑛 ,𝑛𝑛𝑏𝑏 )𝑚𝑚𝑚𝑚𝑥𝑥 (𝑛𝑛𝑛𝑛 ,𝑛𝑛𝑏𝑏 )
𝑚𝑚𝑖𝑖 𝑛𝑛𝑛𝑛 ≠ 𝑛𝑛𝑏𝑏
1 𝑚𝑚𝑖𝑖 𝑛𝑛𝑛𝑛 = 𝑛𝑛𝑏𝑏� (4.7)
This mean that the maximum value of contrast factor is 𝐶𝐶𝐶𝐶 = 1 (high
contrast), and the minimum value is near to zero (low contrast).The contrast
factor is the function of threshold value 𝑡𝑡 , where the threshold value variety
form (0 to 255) the relationship between 𝐶𝐶𝐶𝐶 and 𝑡𝑡 is The logarithmic normal
distribution, this distribution is used when the data have a lower limit where
have the general form:
𝐶𝐶𝐶𝐶(𝑡𝑡 ) = 𝑚𝑚 + 𝑏𝑏. 𝐼𝐼(−0.5(𝑙𝑙𝑛𝑛 (𝑡𝑡 /𝑐𝑐)/𝑑𝑑)2 ) (4.8)
Where 𝑚𝑚, 𝑏𝑏, 𝑐𝑐 and 𝑑𝑑 is a constants dependent on distribution (locally,
globally) contrast and lightness in the image , if we attempt to determine CF
we need a large number of images captured with different lightness levels
moreover have different standard division, figure (4.4) shows the different
Chapter four: Lighting Systems and Suggested Algorithms of Image Quality Assessment and Enhancement
48
thresholds and 𝐶𝐶𝐶𝐶 values for color image appears depending 𝐶𝐶𝐶𝐶 on threshold
edge detection.
The Quality Factor (QF) is determined by area under the curve that is given
by integration the function 𝐶𝐶𝐶𝐶(𝑡𝑡 ) over all threshold value (𝑡𝑡)
𝑄𝑄𝐶𝐶 = ∫ 𝐶𝐶𝐶𝐶(𝑡𝑡 )𝑑𝑑𝑡𝑡 255
0 (4.9)
That is obtained by using a trapezoidal numerical integration rule. Figure
(4.5) shows different 𝑄𝑄𝐶𝐶 values and their curves. We can see the increase 𝑄𝑄𝐶𝐶
due to the increase of area under the curve for image having different lightness
level.
Figure 4.5: Different curves of the QF assessment.
t =50 , CF=0.200
t= 100 , CF= 0.065
t =250, CF= 0.009
t=12 , CF=0.994
Figure 4.4: Original image and its Edge detection in different thresholds and Contrast Factor
(CF) values.
Chapter four: Lighting Systems and Suggested Algorithms of Image Quality Assessment and Enhancement
49
We can summarize adaptive method of image equality assessment
depending on QF by general steps in figure (4.6).
4.4 Contrast and lightness enhancement algorithms In this study, we suggest two algorithms to enhance the color images based
on lightness and contrast changing; they are the adapted Histogram equalization
(AHE) and modified retinex (MR).
4.4.1 Adaptive histogram equalization (AHE) algorithm
The first step in this algorithm is transforming color image from basic RGB
color space to YIQ color space, the forward transform is given by equation
(2.18). In the second step is transforming normalized lightness value using
sigmoid function that is given by:
)1
1/(1nI
nInS
−+= (4.10)
Input color image
Edge detection in different thresholds values
Estimation lightness component
Measure Contrast Factor for each image edge
Determine Quality Factor
Figure 4.6: Steps of QF assessment.
Chapter four: Lighting Systems and Suggested Algorithms of Image Quality Assessment and Enhancement
50
Figure (4.7) shows the relationship between input lightness IRnR versus output
lightness SRnR. The third step is applying HE on modify lightness component, the
processing lightness component Y RPR has been gotten form this step. Finally
inverse transformation from YIQ to RGB color space calculated in YRpRIQ that is
given by [51]:
+−=
−−=
++=
qipybqipygqipyr
703.1106.1647.0272.0621.0956.0
(4.11)
This algorithm has been reduced color shift compared with traditional HE
due to processing lightness component and in the same time enhance lightness
component dependent on sigmoid transform.
This can be achieved by the following steps:
1. Input color image C(x,y,i), i=1,2,3 ( red ,green, blue) components.
2. Transform color image from RGB color space toYIQ and estimation Y
component.
3. Normalized Y component by 𝐼𝐼𝑛𝑛 = 𝑌𝑌255
.
4. Transform lightness by using )1
1/(1nI
nInS
−+= .
Figure 4.7: Relationship between input lightness
versus output lightness in AHE.
Chapter four: Lighting Systems and Suggested Algorithms of Image Quality Assessment and Enhancement
51
5. Applied HE on transformed lightness component sRnR getting processed
lightness component yRpR.
6. Transform color image from YRpRIQ to RGB to get out put image.
4.4.2 Modify retinex (MF) algorithm
In the MSRCR the color value of a pixel is computed by taking the ratio of
the pixel to the weighted average of the neighboring pixels. One disadvantage of
this technique is that there could be abnormal color shifts because three color
channels are treated independently. An inherent problem in most retinex
implementation is the strong ’halo’ effect in regions having strong contrast. The
’halo’ effects are shown in Figure (4.8). The ’halo’ effects and color shifts are
reduced in MF algorithm by processing Y component, the steps of it is:
a. Lightness enhancement
Transform color image from basic RGB color space to YIQ color space by
used equation (3.15). And then transformed normalized lightness value using
sigmoid function given in eq. (4.10).
b. Chromatic enhancement
Figure 4.8: Halo effect caused during retinex enhancement can be
observed around the edge of the body and the background.
Chapter four: Lighting Systems and Suggested Algorithms of Image Quality Assessment and Enhancement
52
The chromatic channels i,q are enhanced by applying MSRCR on the original
RGB components after using forward transformation ; this method of
processing will decrease the color shift ,but it needs to redefined gain-offset
value, in this work used value 0.1 and 0.5, are respectively.
c. Combining enhanced channels
The final step is combining the lightness enhanced component yRpR and
chromatic enhanced channels iRpR,qRpR by using inverse transform(equation 4.11) to
get enhancement components rRp R,gRpR ,bRpR .Figure (4.9) shows the summarization
steps of the proposed algorithm.
We do this by the following steps:
1. Input color image C(x,y,i), i=1,2,3 ( red ,green, blue) components
Input original
RGB image
Transform to
YIQ
Y-extraction
Tone mapping
Applied MSR
Applied MSRCR
Transform to
YIQ
IQ-extraction
Transform to
RGB
Output
enhanced image
Figure 4.9: flowchart of modified retinex (MR)
algorithm.
Chapter four: Lighting Systems and Suggested Algorithms of Image Quality Assessment and Enhancement
53
2. Transform color image from RGB color space to YIQ and estimation Y
component.
3. Normalized Y component by 𝐼𝐼𝑛𝑛 = 𝑌𝑌255
.
4. Transform lightness component by using )1
1/(1nI
nInS
−+= getting processed
lightness component YRpR. 5. Apply MSRCR on transformed original image in step1 getting RRRRGRRRBRRR components 6. Transform color image from RRRRGRRRBRRR to YIQ to getting YRRRIRRRQRRR.
7. Collection component YRp and RIRRRQRRR , then applied inverse transformation to
basic RGB color space to getting output image.
Chapter five: The Results and Discussion
54
5.1 Introduction This chapter includes the most important results that are obtained. Section two
includes distribution of the illuminance for the LEDs and MH system, many
images captured for gray test image to study the distribution of illuminance for
these systems. Section three deals with color image analysis by using quality
assessments for different lightness and contrast levels. These images are
captured under the LEDs lightning system in many cases from low lightness to
moderate lightness, and are also captured with MH lightness system in many
cases from moderate lightness to high lightness. Section four contains color
image enhancement in different lightness levels, twelve images are captured
under the LEDs and MH system are considered. All these images have been
enhanced depending on HE, AINDANE, MSRCR, MR and AHE. In order to
determine the best method of enhancement, equality assessments are taken into
account using CF, EFD, QF, Mean of locally (µ ,σ ) model and histogram
distribution.
5.2 Determining the Distribution of Illuminance Illuminance distribution has been concluded by capturing different images
of gray test image with varied illuminance in two lighting system as following:
5.2.1 Illuminance Distribution in the LEDs Lighting system
To recognized the illuminance distribution for one chip of white LED, the
distance between the gray test image and camera lens is changed step by step
(from 5cm to 50cm) that leads to decrease the illuminance. Figure (5.1) shows
the relationship between the distance and the maximum illuminance, we can
note decreasing the illuminance occurred due to increase the distance, according
to inverse square law of the intensity. To study the distribution of the
illuminance for one ship white LED we can lit the gray test image by this LED
Chapter five: The Results and Discussion
55
at the same time we increase the distance between the lighting source and the
gray image this leads to decreasing the illuminance.
If we capture these images and then take CIELA color transform ,we can
get the illuminance components of these images ,that is shown in figure (5.2)
which represent the 2D and 3D representation of the illuminance and gray test
image was lighted by one ship white LED. The distribution of illuminance is the
Gaussian distribution and its peak decreasing directly with decreasing the
illuminance. Figure (5.4) is shows 3D representation of the Gaussian fitting of
the illuminance for gray test image which is lighted by LEDs system at max.
illuminance( 42.5 lux) and the original curve, the function of illuminance
is where (a=29.8 ,b=78.8 ,c=27.8
,d=12.8 ,e=31.7, f=12.8 ). In this work 28 images are captured for three groups
(a,b and c) under the illuminance varied from low to moderate illuminance.
Table (5.1) illustrates the number of LEDs and corresponding values of
illuminance for 28 levels. Figure (5.3) is shows the distributions of illuminance
for these images in figure (5.5, 5.6 and 5.7), and in figure (5.3) the distribution
of illuminance was remained Gaussian distribution, and diffusion of the light
and peak increasing directly with the increase of illuminance.
Figure 5.1: The relationship between the distance and the
maximum illuminance for one ship white LED.
Chapter five: The Results and Discussion
56
704 lux
314 lux
119 lux
66 lux
44 lux 8.4 lux
11.2 lux
15.3 lux
23 lux
31 lux
Figure 5.2: 2D and 3D representation of the illuminance (illuminance value was got from CIELAB
color space for the gray image), and gray image was lighted by one chip white LED in different
illuminance (maximum illuminance in the real world) levels. A gradient in the illuminance is
accrues by increasing the distance between LEDs board and gray test image.
Chapter five: The Results and Discussion
57
7.5 lux
11.2 lux
24.7 lux
29.4 lux
13.7 lux 31.3 lux
42.5 lux
3.9 lux 20.7 lux
15.2 lux
Figure 5.3: 2D and 3D representation of the illuminance (illuminance value was got from CIELAB color
space for the gray image test), and gray test image was lighted by multi white LEDs. A gradient in the
illuminance is accrues by switches the chips of LEDs gradually (number of LEDs are
1,2,3,…10,15,20,…90 and 98 ).
Chapter five: The Results and Discussion
58
57.5 lux
79.4 lux
93.9 lux
103.2 lux
164.1 lux
154.8lux
124.8 lux
133.5 lux
55.1 lux 18.2 lux
Figure 5.3: continue.
Chapter five: The Results and Discussion
59
112.2 lux
124.6 lux
172.2 lux
182.7 lux
202 lux
214 lux
244 lux
196.2 lux
Figure 5.3: continue.
Figure 5.4: In (a). 3D representation of the illuminance for gray test image which is
lighted by LEDs system at max. illuminance= 42.5 lux and (b). 3D Gaussian fitting of a.
(a) (b)
Chapter five: The Results and Discussion
60
a1
a2
a3
a5
a4
a6
a11
a12
a7
a13
a8
a9
a14
a15
a10
Figure 5.5: The first group of the images lighted by white LEDs with different illumunance levels.
Chapter five: The Results and Discussion
61
a16
a20
a21
a24
a17
a25
a26
a22
a18
a23
a27
a19
a28
Figure 5.5: continue.
Chapter five: The Results and Discussion
62
Figure 5.6: The second group of the images lighted by white LEDs with different illuminance levels.
b15
b10
b5
b6
b11
b12
b7
b3
b8
b9
b14
b1
b2
b3
b4
Chapter five: The Results and Discussion
63
b
Figure 5.6: continued.
b16
b20
b21
b24
b17
b25
b26
b22
b18
b23
b27
b19
b28
Figure 5.6: continue.
Chapter five: The Results and Discussion
64
Figure 5.7: The third group of the images lighted by white LEDs with different illuminance levels.
c15
c10
c5
c6
c11
c12
c7
c13
c8
c9
c14
c1
c2
c3
c4
Chapter five: The Results and Discussion
65
Figure 5.7: continue.
c16
c20
c21
c24
c17
c25
c26
c22
c18
c23
c27
c19
c28
Chapter five: The Results and Discussion
66
5.2.2 Illuminance Distribution in the MH lighting system
To determine the illuminance distribution for MH lighting system, the distance
between the gray image and MH lamp (Projector) is changed step by step (from
40 cm to 490 cm with step 20) that leads to a decrease in illuminance. Figure
(5.8) shows the relationship between the distance and the maximum illuminance,
in this relationship the illuminance decreasing with the increasing distance
according to inverse square law. As in the last section the gray image test has
been captured at this illuminance levels from high (12480 lux) to moderate (220
lux), figure (5.9) illustrates 2D and 3D representation of the illuminance and
gray test images for these level of the illuminance.
No. of LEDs 1 2 3 4 5 6 7 8 9 10 15 20 25 30
Max. Illuminance
(lux) 3.9 7.5 11.2 13.7 15.2 18.2 20.7 24.7 29.4 31.3 42.5 55.1 57.5 79.4
No. of LEDs 35 40 45 50 55 60 65 70 75 80 85 90 95 89
Max. Illuminance
(lux) 93.9 103.2 112.2 124.6 133.5 142.8 154.8 164.1 172.2 182.7 196.5 202 214 224
Table 5.1: The number of switch on LEDs and its illuminance values for
28 levels that have gradient from minimum to moderate level.
Figure 5.8: The relationship between the distance and the
maximum illuminance for MH lamp.
Chapter five: The Results and Discussion
67
220.4 lux 400.1 lux
290.5 lux 584.8 lux
312.5 lux 657.3 lux
240.7 lux 450.6 lux
357.6 lux 788.3 lux
236.8 lux 511.2 lux
Figure 5.9: 2D and 3D representation of the illuminance (illuminance value was got from CIELAB color
space for the gray image test), and gray image was lighted by MH lamp. A gradient in the illuminance is
accrues by increasing the distance between Projector and gray sense.
Chapter five: The Results and Discussion
68
Figure 5.9: continue.
3870.4 lux 1117.3 lux
931.7 lux 2843.0 lux
1362.9 lux 5495.3 lux
2164.1 lux 12480.1 lux
8159.6 lux 1696.6 lux
Chapter five: The Results and Discussion
69
The distribution of illuminance approximately is a plane in the area of
projection (52×40 cmP
2P) in the real world, figure (5.10) shows the 3D illuminance
for gray test image which is lighted by MH lightness system at max. illuminance
(220.4 lux) and plane fitting of this distribution at where
(a=81.25,b=0.01,c=0.01). This distribution is a special case of the Gaussian
distribution in the large area. In this system, we capture three groups images
each one have 22 images, its illuminance was changed from moderate to high as
shown in figures (5.11, 5.12 and 5.13) .
Figure 5.10: In (a) 3D representation of the illuminance for gray test image which is lighted by
MH lightness system at max. illuminance= 220.4 lux and (b). 3D plane fitting of a.
(a) (b)
Chapter five: The Results and Discussion
70
Figure 5.11: The first group of the images lighted by MH lighting lamp with different illumunance
levels.
d11
d7
d8
d12
d3
d4
d5
d9
d1
d10
d6
d2
Chapter five: The Results and Discussion
71
Figure 5.11: continue.
d21
d18
d22
d15
d16
d19
d13
d20
d17
d14
Chapter five: The Results and Discussion
72
Figure 5.12: The second group of the images lighted by MH lighting lamp with different illuminance
levels.
e11
e7
e8
e12
e3
e4
e5
e9
e1
e10
e6
e2
Chapter five: The Results and Discussion
73
Figure 5.12: continue.
e21
e18
e22
e15
e16
e19
e13
e20
e17
e14
Chapter five: The Results and Discussion
74
Figure 5.13: The third group of the images lighted by MH lighting lamp with different illuminance
levels.
f11
f7
f8
f12
f3
f4
f5
f9
f1
f10
f6
f2
Chapter five: The Results and Discussion
75
Figure 5.13: continue.
f21
f18
f22
f15
f16
f19
f13
f20
f17
f14
Chapter five: The Results and Discussion
76
5.3 Results of the Images Quality Assessments Image quality assessment based on lighting change has been determined in
the two lightness levels, the first grades from low to moderate lightness level by
using LEDs lighting system, where the different images (groups a,b and c) are
capture in these levels of illuminance, and the second is grades from moderate to
high lightness level by using MH lighting system (groups d,e and f). Images
quality has been determined by using EFD, Mean of locally (µ ,σ ) model and
CM , QF and. SSIM ( for LED lighting system only) as following:
5.3.1 The entropy of the first derivative image (EFD)
Figures (5.14) and (5.15) show the relationship between maximum
illuminance and EFD for three groups with images captured by LED and MH
lighting system respectively, generally we can be noted:
1. This relation is not linear.
2. In the LED lighting system (from low to moderate lightness levels) the
fluctuation increases in the low illuminance levels (smaller than 50 lux),
then, the increasing become low above this value of the illuminance.
3. For MH lighting system (from moderate to high lightness levels) , the EFD
increases with increase of illuminance, then it reaches a maximum value
(threshold value between 1000 and 1500 lux) and then the EFD decreases
with the increase of illuminance.
5.3.2 The Mean of locally (µ ,σ ) model
Figures (5.16) illustrates the 2D representation of mean of locally (µ ,σ )
model for three groups with images captured by LEDs lightness system and
figure (5.17) shows the 3D representation of this model as the terms of
maximum illuminance, in figure (5.16) this relationship is semi linear and the
mean of image increases with increasing the mean of locally standard deviation,
the doted points value is the maximum illuminance (third dimension in the 3D
representation) where the increase of illuminance will cause the tendency of
points to the optimal region(according to figure 3.3 page 35).
Chapter five: The Results and Discussion
77
Figure 5.14: Relationship between maximum illuminance and entropy of the first derivative for all
groups (a,b and c) images that are captured with the LEDs lighting system.
Group (a)
Group (b)
Group (c)
Chapter five: The Results and Discussion
78
Group (d)
Group (e)
Group (f)
Figure 5.15: Relationship between maximum illuminance and entropy of the first derivative for all
groups (d,e and f ) images that are captured with MH lighting system.
Chapter five: The Results and Discussion
79
Group (a)
Group (b)
Group (c)
Figure 5.16: The 2D representation of the Mean of locally (µ ,σ ) model for all groups (a,b and c ) images
that are captured with the LEDs lighting system, the values beside the dotted points are the maximum
illuminance .
Chapter five: The Results and Discussion
80
Group (a)
Group (b)
Group (c)
Figure 5.17: The 3D representation of the Mean of locally (µ ,σ ) model in the terms of maximum
illuminance for all groups (a,b and c ) images that are captured with the LEDs lighting system.
Chapter five: The Results and Discussion
81
Whereas, figures (5.18) and (5.19) show the 2D and 3D representation of the
Mean of locally (µ ,σ ) model of three groups with images MH lighting system,
in figure (5.18) the relationship between mean of locally standard deviation and
mean of image is not linear. And in 2D and 3D the illuminance with levels (from
1000 lux to 1500 lux) is tends to the optimal region.
5.3.3 Colorfullness Metrics (CM)
Figures (5.20) and (5.21) show the relationship between maximum
illuminance and CM for three groups with images captured under LED and MH
lighting system respectively, generally this relation is not linear, fluctuated at
low illuminance levels (in the LEDs lightnig system). And the CM increases
with increasing the illuminance in the moderate lightness levels (in the LEDs
and MH lighting system), while in the high lightness levels for MH lighting
system there are threshold value of illuminance which lies between (1500 to
2000 lux) became the CM was maximum.
5.3.4 Quality Factor (QF) assessment
Figures (5.22) and (5.23) show the relationship between maximum
illuminance and QF for three groups with images captured under LEDs and MH
lighting system respectively. Generally in figures (5.22) the QF increases
with increasing the illuminance and this relation is not linear with low
fluctuation. In figure (5.23) as in EFD and CM there are threshold value of
illuminance which lies between (1500 to 2000 lux) became the QF was
maximum.
5.3.5 Stricture Similarity Index (SIMM)
Figures (5.24) shows the relationship between maximum illuminance and
SSIM for three groups with images captured by LEDs lighting system, generally
the SSIM increases with increasing the illuminance and this relation is not linear
and like the QF.
Chapter five: The Results and Discussion
82
Group (d)
Group (e)
Group (f)
Figure 5.18: The 2D representation of The Mean of locally (µ ,σ ) model for all groups (d,e and f ) images
that are captured with the MH lighting system, the values beside the dotted points are the maximum
illuminance .
Chapter five: The Results and Discussion
83
Group (d)
Group (e)
Group (f)
Figure 5.19: the 3D representation of The Mean of locally (µ ,σ ) model in the terms of maximum
illuminance for all groups ((d,e and f )) images that are captured with the MH lighting system.
Chapter five: The Results and Discussion
84
Group (a)
Group (b)
Group (c)
Figure 5.20: Relationship between maximum illuminance and color fullness index for all groups (a,b
and c ) images that are captured with the LEDs lighting system.
Chapter five: The Results and Discussion
85
Group (d)
Group (e)
Group (f)
Figure 5.21: Relationship between maximum illuminance and color fullness index for all groups (d,e
and f ) images that are captured with the MH lighting system.
Chapter five: The Results and Discussion
86
Group (a)
Group (b)
Group (c)
Figure 5.22: Relationship between maximum illuminance and Quality factor for all groups (a,b and c)
images that are captured with the LED lighting system.
Chapter five: The Results and Discussion
87
Group (d)
Group (e)
Group (f)
Figure 5.23: Relationship between maximum illuminance and Quality factor for all groups (d,e and f)
images that are captured with the MH lighting system.
Chapter five: The Results and Discussion
88
Group (a)
Group (b)
Group (c)
Figure 5.24: Relationship between maximum illuminance and structure similarity index(SSIM) for all
groups (a,b and c) images that are captured with the LED lighting system.
Chapter five: The Results and Discussion
89
5.4 The Results of Images Enhancement In this work, five algorithms (HE, MSRCR, AINDANE AHE and MR have
been used to enhance color images with different lightness and contrast level ,
these images are captured under different luminance levels by used LEDs and
MH lighting systems. In this section twelve images (six with low and moderate
lightness levels and other six with moderate and high lightness levels) have been
enhanced as follows: 5.4.1 Color images enhancement with low and moderate lightness levels
These image are captured under LEDs lighting system, six images with low
and moderate lightness level have been used to enhancement. Figure (5.25)
illustrates these images and their histogram, moreover the values of illuminance
at which they are captured. In this figure the most recursions in the histogram
for the original images with low lightness belong to the low value of the
intensity, whereas in the images with moderate lightness levels, we can see the
distributions have covered the most intensity levels. Figure (5.26) shows the
images and their histograms which have been enhanced by AINDANE
algorithm. The histograms of these images are reduced in the middle value of
the intensity. The images enhancement by HE algorithm and the histogram of
these have been illustrated in figure (5.27). We can note that this distribution is
semi linear with low fluctuation. If we compare this distribution with the
distribution of images enhancement by AHE in figure (5.28), there is a similarity
between them except in the high intensity level of images enhancement by AHE,
this distribution will be increase and will become more fluctuated.
Figures (5.29) illustrates the images enhancement by MSRCR algorithm image
and their histograms, the distribution of these histograms are increased in the
low and moderate intensity levels. And the distribution for the images
enhancement by MR algorithm in figure (5.30), will be increased in the
moderate
Chapter five: The Results and Discussion
90
(1) 20.7 lux (2) 224 lux (3) 31.3 lux
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) 20.7 lux (5) 224 lux (6) 31.3 lux
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Figure 5.25: The first and third rows, images with low and moderate lightness levels that is
used to enhancement and their histogram in the second and fourth rows, these images are
captured by LEDs lightness.
Chapter five: The Results and Discussion
91
Figure 5.26: The first and third rows, images with low and moderate lightness that are
enhanced by AINDANE algorithm and their histogram in the second and fourth rows.
(1) (2) (3)
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) (5) (6)
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Chapter five: The Results and Discussion
92
(1) (2) (3)
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) (5) (6)
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Figure 5.27: The first and third rows, images with low and moderate lightness that are
enhanced by HE algorithm and their histogram in the second and fourth rows.
Chapter five: The Results and Discussion
93
(1) (2) (3)
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) (5) (6)
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Figure 5.28: The first and third rows, images with low and moderate lightness that are
enhanced by AHE algorithm and their histogram in the second and fourth rows.
Chapter five: The Results and Discussion
94
(1) (2) (3)
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) (5) (6)
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Figure 5.29: The first and third rows, images with low and moderate lightness that are enhanced by
MSRCR algorithm and their histogram in the second and fourth rows.
Chapter five: The Results and Discussion
95
(1) (2) (3)
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) (5) (6)
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Figure 5.30: The first and third rows, images with low and moderate lightness that are
enhanced by MR algorithm and their histogram in the second and fourth rows.
Chapter five: The Results and Discussion
96
and high lightness level, this mean that the enhancement of lightness became
the best. In the objective assessments of images enhancement by different
algorithms we can note that all method have been succeeded to enhancement,
but we can see in AINDANE algorithm the color shift is high and in HE
algorithm the images with low lightness go to gray. The mean of locally (µ, σ)
model has been illustrated in figure (5.31) for images with low and moderate
lightness that is enhanced by different algorithms. We can see, the most points
in the optimal regions (increase in contrast and lighting) belong to MH
algorithm then follows MSRCR, AHE and HE. But, the AINDANE algorithm
enhances the lightness without the contrast, this behavior is reflected in table
(5.2) that represents the value of EFD, CM and QF for the original and enhance
images. From this value, we can see that the best algorithm to enhancements
color images with low and moderate lightness levels is MR due to higher values
of EFD, CM and QF, and then MSRCR is followed. If a comparison between
the HE, AHE and AINDANE is done, we can see the performance of AHE
algorithm is the best.
5.4.2 Color images enhancement with moderate and high lightness levels
These image captured by MH lighting system, six images with moderate and
high lightness levels are used to enhancement. Figure (5.32) shows these
images, their histogram and the values of illuminance at which they are
captured. In this figure the most frequency value in the histogram for the
original images with high lightness belong to the high value of the intensity,
however in the images with moderate lightness levels we can see that the
distributions have been covered the most intensity levels. Figure (5.33) shows
the images and their histograms which have been enhanced by AINDANE
algorithm. The histograms of these images are reduced in the middle value of
the intensity and some values have large peaks.
Chapter five: The Results and Discussion
97
Image 2
Figure 5.31 : The Mean of locally (µ ,σ ) model for image enhancement by HE ,MSRCR ,AINAANE ,MR and AHE , these image capture by the LEDs lighting system.
Image 1
Image 3
Chapter five: The Results and Discussion
99
AHE MR AINDANE MSRCR HE Original QUALITY
TYPE
16.515 30.367 25.703 30.211 14.983 12.556 CM
5.369 5.915 5.207 5.936 5.188 4.817 EFD
39.427 61.669 26.247 61.658 34.614 14.068 QF
Image 1
AHE MR AINDANE MSRCR HE Original QUALITY
TYPE
24.047 31.185 18.965 30.607 21.437 12.871 CM
5.507 5.890 5.042 5.875 5.369 4.272 EFD
43.679 53.575 21.158 54.490 38.030 16.913 QF
Image 3
AHE MR AINDANE MSRCR HE Original QUALITY
TYPE
40.826 46.101 39.393 47.737 36.431 33.539 CM
5.929 6.355 5.520 6.331 5.731 5.439 EFD
56.483 75.83 32.794 72.503 48.235 34.676 QF
Image 2
Table 5.2: The values of CM, EFD and QF of the original images that is captured by
LEDs lighting system and they are enhanced by the HE, MSRCR, AINDANE, MR and
AHE.
Chapter five: The Results and Discussion
100
The enhanced images by HE algorithm and their histograms are presented in
the figure (5.34). We can note these distributions are homogeneous for all
intensity levels and with low fluctuation. If we compare this distribution with
AHE MR AINDANE MSRCR HE Original QUALITY
TYPE
14.493 22.077 16.395 21.901 13.807 12.282 CM
4.972 5.488 4.861 5.377 4.849 4.556 EFD
29.965 42.309 19.218 39.568 26.212 13.881 QF
Image 5
AHE MR AINDANE MSRCR HE Original QUALITY
TYPE
37.990 38.524 28.551 38.947 33.331 27.001 CM
5.491 5.828 4.916 5.673 5.321 4.985 EFD
43.587 54.187 21.842 49.821 38.539 29.227 QF
Image 4
AHE MR AINDANE MSRCR HE Original QUALITY
TYPE
39.277 49.072 35.151 48.009 35.612 33.551 CM
5.410 5.504 4.857 5.307 4.902 4.596 EFD
30.733 42.884 20.231 36.790 27.794 22.077 QF
Image 6
Table 5.2: continued.
Chapter five: The Results and Discussion
101
the distribution of images enhanced by AHE ,shown in figure (5.35); there is a
similarity between them except in the high intensity level of the images
enhancement by AHE, the distribution well be increased in some points and the
distribution will become more fluctuated. And we can see in the two method a
decrease of the frequency in the value of high intensity if they compared with
the histogram of the original images. Figure (5.36), illustrates the images
enhancement by MSRCR algorithm and their histograms, the distribution of
these histograms have been decreased in the high intensity levels ,and the
distributions for the images enhancement by MR algorithm in figure (5.37)
generally, is similar to distribution in the MSRCR algorithm. If we use the
objective assessments as criterion of images enhancement, we can say that in the
color shift is increased in the AINDANE algorithm and halo affected appears
spatially in the MSRCR and HE, though the algorithms AHE,MR,MSRCR and
HE succeed to enhance these images. The mean of locally (µ ,σ ) has been
illustrated in the figure (5.38) for images with low and moderate lightness that is
enhanced by different algorithms. The point of the original images that is
captured with high illuminance levels have high lightness value but with low
contrast, after they enhancement they had low lightness but high contrast.
Generally, we can note the most points in the optimal regions are decrease in the
lightness but increase in the contrast in the original images enhancement with
high lightness, but increasing in the contrast and lighting in the original image
enhancement with moderate lightness. This region belongs to MR and AHE
algorithms, and then follows MSRCR, HE and AINDANE algorithms. Table
(4.3) shows the value of EFD, CM and QF for the original and enhancement
images. We can see from these values that the best algorithms to enhance color
images with high and moderate lightness levels are the MR and AHE due to
higher values of EFD,CM and QF ,then MSRCR ,HE and AINDANE are after
them.
Chapter five: The Results and Discussion
102
(1) 220.4 lux (2) 12480.1 lux (3) 220.7 lux
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) 8159.6 lux (5) 236.8 lux (6) 511.2
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Figure 5.32: the first and third rows, images with low and moderate lightness levels that is
used to enhance and their histogram in the second and fourth rows, these images are
captured by MH lightness system.
Chapter five: The Results and Discussion
103
(1) (2) (3)
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) (5) (6)
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Figure 5.33: The first and third rows, images with moderate and high lightness that are
enhanced by ANDAN algorithm and their histogram in the second and fourth rows.
Chapter five: The Results and Discussion
104
(1) (2) (3)
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) (5) (6)
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Figure 5.34: The first and third rows, images with moderate and high lightness that are
enhanced by HE algorithm and their histogram in the second and fourth rows.
Chapter five: The Results and Discussion
105
(1) (2) (3)
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) (5) (6)
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Figure 5.35: The first and third rows, images with moderate and high lightness that are
enhanced by AHE algorithm and their histogram in the second and fourth rows.
Chapter five: The Results and Discussion
106
(1) (2) (3)
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) (5) (6)
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Figure 5.36: The first and third rows, images with moderate and high lightness that are
enhanced by MSRCR algorithm and their histogram in the second and fourth rows.
Chapter five: The Results and Discussion
107
(1) (2) (3)
(a) hist. of (1) (b) hist. of (2) (c) hist. of (3)
(4) (5) (6)
(d) hist. of (4) (e) hist. of (5) (f) hist. of (6)
Figure 5.37: The first and third rows, images with moderate and high lightness that are
enhanced by MR algorithm and its histogram in the second and fourth rows.
Chapter five: The Results and Discussion
108
Image 1
Image 2
Image 3
Figure 5.38 : The Mean of locally (µ ,σ ) model for image enhancement by HE ,MSRCR ,AINAANE ,MR and AHE , these image capture by the MH lighting system.
Chapter five: The Results and Discussion
110
AHE MR AINDANE MSRCR HE Original QUALITY
TYPE
36.640 34.3548 34.847 37.627 29.717 33.050 CM
5.8748 6.044 5.525 5.933 5.817 5.530 EFD
53.911 66.3471 37.633 62.963 53.878 44.634 QF
AHE MR AINDANE MSRCR HE Original QUALITY
TYPE
44.875 34.9482 35.727 38.047 30.289 34.521 CM
6.355 6.303 5.595 6.064 6.339 5.832 EFD
72.323 79.1476 43.478 71.979 72.296 57.058 QF
AHE MR AINDANE MSRCR HE Original QUALITY
TYPE
55.556 46.5773 39.326 48.637 38.032 38.672 CM
4.852 4.911 4.313 4.789 4.895 4.473 EFD
27.644 29.3224 16.139 26.065 27.116 19.723 QF
Image 1
Image 2
Image 3
Table 4.3: The values of CM, EFD and QF of the original images that are captured by
MH lighting system and they are enhanced by the HE, MSRCR, AINDANE, MR and AHE.
Chapter five: The Results and Discussion
111
AHE MR AINDANE MSRCR HE Original QUALITY
TYPE
53.913 45.6017 38.564 47.742 38.364 37.129 CM
5.207 5.013 4.317 4.895 5.337 4.589 EFD
39.034 31.8003 16.941 28.519 38.007 21.297 QF
MHE MR AINDANE MSRCR HE Original QUALITY
TYPE
30.725 34.6014 32.770 39.891 27.581 30.887 CM
5.598 6.024 5.545 5.857 5.546 5.484 EFD
43.632 61.8811 34.228 57.020 42.291 40.564 QF
AHE MR AINDANE MSRCR HE Original QUALITY
TYPE
37.631 38.6453 34.800 44.156 32.885 33.314 CM
5.994 6.313 5.756 6.130 5.906 5.820 EFD
56.188 74.6554 41.273 68.182 54.344 51.003 QF
Image 4
Image 6
Image 5
Table 4.3: continued.
Chapter Six : Conclusions and Suggestion Future Works
112
6.1 Conclusions In this research we study the distribution of lighting for LEDs and MH
lighting sources and capture six groups of images with different illuminance
(contrast and lightness) which have been used to determine the best method to
quality image assessment based on lightness and contrast change. And then
different images with different lighting and contrast have been enhanced by
using many traditional and suggested algorithms.
From the results of the present study the following points were concluded:
1. The lightness distribution for the LEDs lamp is the Gaussian distribution,
whereas the distribution in the MH lamp is plane at near distance (about 2.5 m
or less from the object).
2. The increase in the illuminance (lightness) in which images are captured with,
does not mean necessarily increasing in the quality of the image thus, there are
illuminance levels that make the image having high quality which lays between
(1500 to 2000 lux).
3. Belong of the image quality assessment, the methods CM and EFD does not
measure images with low and high lightness levels accurately.
4. The mean of locally (µ ,σ ) model is good method to determine the quality of
images with different lightness levels: low ,moderate and high ,but the drawback
in this method does not determine the numerical value.
5. The suggested method, QF is robust method to assessment quality of images
with different lightness levels.
6. The MR and MSRCR are good algorithms to enhance the color images with
low and moderate lightness levels.
7. In the images with high lightness levels, the MR and AHE algorithm are the
best methods to enhance these images.
Chapter Six : Conclusions and Suggestion Future Works
113
8. The suggested algorithm MR has been succeeded to enhance color images
with different lightness levels (low, moderate and high). Also it appears the best
result if it is compared with other algorithms such (MSRCR, HE, AINDANE).
6.2 Suggestions Future Works
Some proposals for future works derived from this project are demonstrated in
following points which are concerned with different image quality assessment
and color image enhancement based on lightness change:
1. Aerial image enhancement depending on MR and AHE algorithms.
2. Developing MSRCR algorithm by using color space transformation.
3. Studying the image quality assessment in different illuminans levels by
using Macbeth chart test.
4. Deterring the coefficients of QF by using Macbeth chart test.
5. Medical image enhancement depending on MR algorithm based on
segmentation.
6. Foggy and dusty image enhancement depending on MR algorithm.
114
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