Multiterminal source coding for multiview images underwireless fading channels

Chadi Khirallah & Vladimir Stankovic & Lina Stankovic &

Samuel Cheng

Published online: 7 July 2009# Springer Science + Business Media, LLC 2009

Abstract This paper addresses the problem of wireless transmission of a captured scenefrom multiple cameras, which do not communicate among each other, to a joint decoder.Correlation among different camera views calls for distributed source coding for efficientmultiview image compression. The fact that cameras are placed within a short range of eachother results in a high level of interference, multipath fading, and noise effects duringcommunications. We develop a novel two-camera system, that employs multiterminalsource coding and complete complementary data spreading, so that while the formertechnique exploits the statistical correlation between camera views, and performs jointcompression to reduce transmission rates, the spreading technique will protect transmitteddata by mitigating the effects of wireless fading channels. Our results indicate that theproposed system is competitive when compared to two independently JPEG encodedstreams at low to medium transmission rates.

Keywords Multiview image coding . Distributed source coding . Slepian-Wolf coding .

Spread spectrum . Complete complementary sequences

Multimed Tools Appl (2010) 48:457–470DOI 10.1007/s11042-009-0313-9

This work is supported by EPSRC Research Grant EP/E021964/1.

C. Khirallah (*) : V. Stankovic : L. StankovicDepartment of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UKe-mail: chadi.khirallah@eee.strath.ac.uk

V. Stankovice-mail: vladimir.stankovic@eee.strath.ac.uk

L. Stankovice-mail: lina.stankovic@eee.strath.ac.uk

S. ChengSchool of Electrical and Computer Engineering, Schusterman Center, University of Oklahoma, Tulsa,OK 74135-2512, USAe-mail: samuel.cheng@ou.edu

AbbreviationsMT-SC Multiterminal source codingJPEG Joint photographic expert groupCC Complete Complementary sequencesSW Slepian WolfWZ Wyner Ziv

1 Introduction

Multiview images, captured synchronously by several closely-located cameras, arestatistically dependent. Exploiting this dependency is desirable for compression. Theconventional approach requires joint source coding of different views at a centralizedpowerful encoder to achieve better compression than separate coding of the images.However, this method is not practical in a distributed source coding scenario, where thecameras have stringent conditions on power and communication bandwidth and must notcommunicate among each other. Distributed source coding (DSC) has emerged as analternative compression technique with separate encoding and joint decoding, based on thetheory of Slepian and Wolf in [10], which states that separate lossless compression of twocorrelated sources and joint decoding suffer no rate loss compared to the case when thesources are compressed jointly. In 1976, Wyner and Ziv [15] extended one special case ofSlepian-Wolf (SW) coding, namely, lossless source coding with decoder side information,to lossy source coding with decoder side information. Unlike SW coding, there is in generala rate loss with Wyner-Ziv (WZ) coding compared to the lossy source coding problemwhen side information is also available at the encoder.

Berger [1] introduced the general problem of Multiterminal source coding (MT-SC) byconsidering a more general case of separate lossy source coding of two (or more) sources.Two classes of the MT-SC problem have been studied in the literature, the case where eachencoder observes directly its source [8]; and the case when the encoder cannot observe thesource directly but is instead provided with a noisy version. These two cases are termeddirect and indirect MT-SC problem, respectively. Considerable research has been focusedon theoretically studying and determining the achievable rate region. Consequently, theexact limits for the indirect quadratic Gaussian MT-SC problem with an arbitrary number ofencoders and the direct quadratic Gaussian MT-SC problem with two encoders appeared in[8] and [14], respectively.

In [16] authors focus on practical code designs for the quadratic Gaussian direct andindirect MT-SC problem with two encoders, and proposed a framework called SW codedquantization (SWCQ) for MT-SC. The main idea is to quantize the sources first usingconventional quantizers and then apply SW coding to exploit remaining correlation amongquantization indices and further reduce the rate. In this setup, SW coding plays the role ofconditional entropy coding.

In this paper, we propose a practical distributed system capable of efficiently compressingtwo images, generated by multiview cameras/terminals without the need for communicationbetween cameras, and transmitting the resulting data over wireless multipath fading channels toa central decoder. Since in practical applications (e.g., sensor network), it is preferable for theencoders to be able to operate at flexible rates, we employ direct MT-SC using the non-asymmetric SW scheme [11], to allow flexible rate allocation between encoders.

The paper is organised as follows. In Sections 2 and 3 we review the concepts of DSC asapplied to image and video compression, and spreading and despreading using complete

458 Multimed Tools Appl (2010) 48:457–470

complementary sequences, respectively. The proposed multiview image communicationsystem is described in Section 4, and simulation results are presented in Section 5. Finally,Section 6 concludes the paper.

2 Distributed source coding

This section presents brief overviews of DSC for image compression and of the direct MT-SC concept. For a more detailed review of both direct and indirect MT-SC, readers arereferred to [16] and references within.

2.1 Distributed source coding for image/video compression

The application of DSC to image/video compression, often called distributed video coding(DVC) or WZ video coding, has been investigated by several research groups (see [5, 6] fortwo review papers). Mono-view DVC exploits correlation among successive frames of thevideo to compress it without the need for complex motion search at the encoder side.Instead, motion search is performed at the decoder side. DVC employs WZ coding withside information extracted from the previously decoded frame.

In [9], a multi-view application is considered and a fusion algorithm, which adaptivelyexploits either temporal or inter-view pixel-to-pixel correlation, is proposed. A stereo videocompression scheme based on symmetric scalar quantization (SQ) for SW coding in pixeldomain is presented in [12]. [17] investigates a stereo matching problem by modifying thebelief propagation algorithm and exploiting SW and WZ coding for compressing transformcoefficients, disparity, and motion vectors.

In [13], an asymmetric WZ technique that uses an unsupervised learning algorithm hasbeen proposed for encoding stereoscopic video/images. One view is compressed using pixeldomain WZ coding, while the other view, compressed using conventional methods anddelivered losslessly to the decoder, was used for generating side information for WZdecoding. This paper proposes a compression scheme that extends the above technique to aDCT domain symmetric direct MT-SC scheme.

2.2 Direct multiterminal source coding

In the direct MT-SC setup [1, 14], two sources X1 and X2 are observed by two terminals,where Terminal i observes Xi without knowing Xj (where i; j 2 1; 2f g). The terminalsseparately compress their observations and send the result over a noisy channel to a jointdecoder, which recovers both X1 and X2 under a distortion constraint. The exact achievablerate region for the general case is still unknown [1]. The quadratic Gaussian problem withtwo encoders was recently solved in [14].

The previous code design proposed in [16] attempts to approach points on the achievablebound [14] by quantizing observations and then applying SW coding on quantizer’s indicesto reduce the rate. The compressed bit streams are sent over a noiseless channel to thedecoder which first performs SW decoding followed by dequantization and estimation torecover both X1 and X2.

We note that the previous designs [16] assume a noiseless and interference-free channelbetween the terminals and the joint decoder. However, for the noisy channel, additionalprotection is necessary since the compressed MT-SC streams are very sensitive to errors.

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3 Complete complementary (CC) sequences

CC sequences are formed using sets of orthogonal binary sequences with ideal correlationproperties. i.e., zero cross-correlation and zero out-of-phase autocorrelation for any relativeshifts between two sequences. CC sequences are proposed as a substitute to the currentlyused CDMA spreading sequences (such as Gold, Walsh Hadamard, and m-sequences),which lose their mutual orthogonality in asynchronous multipath fading channels, andresults in interference-limited system performance. On the other hand, CC sequencesmaintain their orthognality under such channel conditions and greatly improve CDMAsystem performance [4, 7].

To illustrate data spreading and despreading using CC sequences, consider the followingexample. Assuming CC sequences of length kCC equal to four chips, then from [4] the CC setsize N ¼ ffiffiffiffiffiffiffi

kCCp ¼ 2 and the total number of available sets F=2. In this case, it is possible to

assign the two CC sets to two terminals; the first set X consists of w11 ¼ þ;þ;þ;�½ � andw12 ¼ þ;�;þ;þ½ �, while the second set Y contains w21 ¼ þ;þ;�;þ½ � and w22 ¼þ;�;�;�½ � (“+” denotes + 1 and “−” is −1). For i; j 2 1; 2f g, let wij � wij denotethe shift-and-add operation for calculating the auto-correlation function for wij. Then, theauto-correlation function for CC sequence sets X and Y are yXX ¼ w11 � w11 þ w12 � w12 ¼0; 0; 0; 8; 0; 0; 0½ � and yYY ¼ w21 � w21 þ w22 � w22 ¼ 0; 0; 0; 0; 0; 0; 0½ �, respectively.Results obtained in [7] show that the performance of systems using CC sequences surpassthat of systems using classical spreading sequences.

In this paper we assume that the spread data streams are sent to the receiver in parallelusing different orthogonal carrier frequencies, such as in multi-carrier (MC) transmission.For generation and characteristics of CC sequences refer to [4, 7]. The fact that MC-CDMAsystem of [4] uses offset stacked (OS) spreading modulation technique, which enablesmultirate transmission without the need for a complex rate matching algorithm, makes CCsequences very attractive for multimedia services with variable rate requirements to whichrate change can be made scalable and continuous.

4 Proposed technique

In this section we describe the proposed system shown in Fig. 1. Two identicalencoders are employed at two cameras, which do not communicate with each other. Bothencoders perform a 4×4 integer discrete cosine transform (DCT) on each macroblock(MB). The resulting transform coefficients are arranged according to their position withina MB, in 16 bands one DC and 15 AC and each band is of size 12960 coefficients. DCTcoefficients are then uniformly quantized using six quantization matrices of size 4×4shown in Fig. 2, and are referred to as Qi with i 2 1; . . . ; 6f g. In each quantizationmatrix, the value at each position indicates the number of quantization levels associatedwith the corresponding DCT coefficients band. The higher the values in Qi, the higher thebitrate and quality of reconstructed images. On the other hand, zero value means that noWyner-Ziv bits are transmitted for the corresponding band [3]. In this paper, sixquantization matrices are considered based on empirical results and the simulationsetup. However, other quantization matrices, similar to those presented in [3], can alsobe used.

The resulting quantized symbols are then subjected to bitplane extraction. Due to thesignificant effect of the DC coefficients on the quality of reconstructed images, the DCcoefficients are encoded using a similar approach to that used in the JPEG differential

460 Multimed Tools Appl (2010) 48:457–470

encoder. Each band of the DC bitplanes are input to a differential encoder, which in turnsends the first element and the difference between the odd and even elements in eachband to an arithmetic encoder [2]. This reduces the number of bits passed to thesymmetrical SW encoders. On the other hand, performing differential encoding on the ACcoefficients, which are less correlated than the DC coefficients, do not offer similarreduction in rate.

4.1 Symmetrical SW encoder

Each bitplane is then independently fed into a channel encoder, which generates parityinformation for that bitplane. In the following we explain the operation of the SW encoder.Following [16], let C be an (n, k) linear systematic channel block code withk ¼ 2� r1 � r2ð Þn, where r1 and r2 are the required compression rates for two sources,and the generator matrixG ¼ Ik Pk� n�kð Þ

� �. We partition C into two subcodes, C1 and C2,

defined by generator matrices:

G1 ¼ Im1 Om1�m2 P1½ �; ð1Þand

G2 ¼ Om1�m2 Im2 P2½ �; ð2Þwhich consist of top m1 and bottom m2 rows of G, respectively, where m1 ¼ 1� r1ð Þn andm2 ¼ 1� r2ð Þn, (thus m1 þ m2 ¼ k). The parity-check matrices for C1 and C2 can bewritten, respectively, as:

H1 ¼Om2�m1 Im2 Om2� n�kð Þ

PT1 O n�kð Þ�m2

In�k

" #ð3Þ

H2 ¼Im1 Om1�m2 Om1� n�kð ÞO n�kð Þ�m1

PT2 In�k

" #ð4Þ

DC

T

Qua

ntiz

er I

Bitplane D

iffe

rent

ial

Enc

oder

I

DC

Ari

thm

etic

E

ncod

er I

Symmetric SW Encoder IAC

Ari

thm

etic

Dec

oder

I

Qua

ntiz

er I

I

Bitplane

DC Ari

thm

etic

E

ncod

er I

I

SymmetricSW Encoder II

AC

Ray

leig

h fa

ding

cha

nnel

Sym

met

ric

SW D

ecod

erA

rith

met

icD

ecod

er I

I

DC

T

CC

Spr

eadi

ngC

C S

prea

ding

CC

Des

prea

ding

CC

Des

prea

ding

Deq

uant

izer

ID

equa

ntiz

er I

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IDC

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ncod

er I

I

Dif

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ntia

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ecod

er I

ID

iffe

rent

ial

Dec

oder

II

Fig. 1 Block diagram of the system setup

Multimed Tools Appl (2010) 48:457–470 461

4.1.1 SW encoding

Each bitplane of the AC bands of length-n are partitioned into three parts (which are oflength m1, m2, and n-k), so that the resulting right and left images are:

un ¼ um11 um2

2 un�k3

� � ð5Þ

vn ¼ vm11 vm2

2 vn�k3

� � ð6Þthen the resulting syndrome vectors are:

sn�m11 ¼ unHT

1 ¼ um22 un�k

3 �um11 P1

� � ð7Þ

sn�m22 ¼ vnHT

2 ¼ vm11 vn�k

3 �vm22 P2

� � ð8Þwhich are directly sent to the CC spreading stage, where � is modulo-2 sum.

4.1.2 CC Spreading and despreading

After data compression, encoded streams are spread using orthogonal CC sequences tocombat fading and multipath access interference at the decoder due to the asynchronouscommunication in the fading channel. As mentioned in Section 3, the transmitted CC signalconsists of N ¼ ffiffiffiffiffiffi

kccp

components that are transmitted in parallel over N orthogonal carrierfrequencies. In Fig. 1, Terminal 1 encodes the first view using CC sequences w11 and w12,into C11 and C12 and send them on two frequencies f1 and f2, respectively. Similarly,Terminal 2 encodes the second view using w21 and w22 into C21 and C12, which are alsosent on f1 and f2. Let G be the spreading factor defined as the ratio between length (numberof chips) of the output sequence Cij, i; j 2 1; 2f g, and the length of the chips sent per sourcesymbol from Terminal i and i 2 1; 2f g on both f1 and f2. In CC spreading [7], a set of N CC

Q6Q5Q4

0001600000000

00016000160000

000160001600016

001664001664001664

Q3Q2Q1

000000000000

000000000000

000800040000

008640046400464

Fig. 2 An example of six quantization matrices where the number of quantization levels in each matrix isordered in decreasing rate and increasing distortion

462 Multimed Tools Appl (2010) 48:457–470

sequences is assigned to each spread stream. Hence, for kcc=4, the input stream to the CCspreader is divided into blocks of length four that will be spread into G=7/4, with Terminaltransmission rate Ri=14/4=3.5, and sum-rate for the two terminals R=7 chips per symbol.For example, assuming data streams input to the CC spreader of rate equal 1 Megabit persecond (Mbit/sec), the sum-rate of the two terminals R = 7 (Mchip/sec).

We assume flat fading, and perfect channel knowledge at the decoder. Let h1 and h2denote the channel coefficients from Terminal 1 and Terminal 2 to the decoder, respectively.h1 and h2 are non-correlated, since the two Terminals are placed just far enough to ensurestatistically independent fading. For example, the carrier frequency used is 2 GHz and theseparation distance required between terminal d � l=2 ¼speed of light / (2×carrierfrequency)=0.075 m.

At the decoder the CC despreaders invert the spreading process, and pass the resultingsignals to the arithmetic decoders, to reconstruct the DC bitplanes, and the joint symmetricSW decoder to reconstruct the AC bitplanes of the left and right images.

4.1.3 SW decoding

Upon recovering the syndrome vectors, given in Eqs. (7) and (8), sn�m11 and sn�m2

2 , thedecoder forms an auxiliary length-n row vector as:

sn ¼ vm11 um2

2 un�k3 � vn�k

3

� ��um11 P1 � vm2

2 P2

� � ð9Þ

sn ¼ vm11 um2

2 un�k3 � vn�k

3

� �� um11 vm2

2

� �� P� � ð10Þ

then it finds a codeword cn of the main code C coset (in Hamming distance) to sn. Let thevector um1

1 vm22

� �be the systematic part of cn, and then un and vn are recovered as:

un ¼ um11 G1 � O1�m1 um2

2 un�k3 �um1

1 P1

� �; ð11Þ

and

vn ¼ vm22 G2 � vm1

1 O1�m2 vn�k3 �um2

2 P2

� � ð12ÞFinally, the decoded data streams are converted to decimal values and dequantized

before performing the Inverse DCT (IDCT) process and recovering the originalimages.

5 Simulation results

Simulations are performed using “Teddy” and “Cone” images [http://vision.middlebury.edu/stereo/data/]. Each image is of size 288-by-720 pixels and has 8-bit depth. Differentquantization parameters are used to achieve a trade-off between transmission rates andreconstructed image quality. Additionally, we use a multipath Rayleigh fading channelmodel that is simulated using two independent flat-fading sub-channels, with one chipdelay between them in order to simulate asynchronous signals at the decoder, and additive-white-gaussian-noise (AWGN) of zero-mean and unit variance. The decoder is assumed tobe mobile with speeds 3, 30 and 120 km/h. The same signal-to-noise ratio (SNR) in bothchannels from the two MT-SC encoders to the joint decoder is used.

Multimed Tools Appl (2010) 48:457–470 463

We consider a symmetric MT-SC setup [16] where the MT-SC rates at the terminalsare equal (i.e., R1=R2) and the MT sum-rate R is equal to the sum rate of the transmittedchips per second from both terminals (i.e., R=R1+R2 chips per second (chip/sec)). ForMT-SC, we use uniform scalar quantizer (SQ) plus SW coding based on turbo codes as in[16]. The block length is fixed at 12960 (= 288×720) bits. Two sets of CC sequences areused (with element sequence length kcc=4 chips), with each set assigned to one terminaland the processing gain is 4×2 [4], and for each four input bits, seven chips aretransmitted over two carrier frequencies. We employ the OS spreading modulationtechnique [4].

Figures 3 and 4 show the PSNR as a function of the total transmission sum-rate R,given in Kbit/sec, from both cameras. Quantization matrices shown in Fig. 2 are used.The SNR in the channel from both cameras to the joint decoder is fixed at 30dB, at whichthe bit-error-rate (BER) obtained is less than 10−5, which we consider the case for error-free transmission. We use turbo codes for practical SW coding, and also include resultsobtained assuming: i) ideal SW coding plus practical CC spreading, i.e., the source isquantized with uniform SQ and then compressed using an ideal SW coder; the totalnumber of bits to be spread is equal to the joint entropy of the quantization indices at thetwo encoders/terminals. ii) two independent JPEG encoders, iii) two independentJPEG2000 encoders, and finally iv) joint multi-view H.264 (H.264/MVC).

0 50 100 150 200 250 30029

30

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Sum rate [Kbit/sec]

PSN

R [

dB]

H264 / MVC (Multiview)JPEG2000 JPEG Ideal SW codingPractical MT-SC systemPractical MT-SC system (3km/h)Practical MT-SC system (30km/h)Practical MT-SC system (120km/h)

Q1

Q3

Q2

Q6

Fig. 3 PSNR averaged over the two views, vs. total sum-rate R for the proposed system for AWGN channeland flat-fading channels assuming decoder speeds of 3, 30 and 120 km/h. The “Teddy” stereo image of size288-by-720 is used

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Figures 3 and 4 present fourfold result. First, by decreasing the number of quantizationlevels, the compression efficiency increases, due to the increased spatial similarity/correlation between the bitplanes of both views. However, the reconstructed image qualityis reduced. For example, in Fig. 3, decreasing the number of quantization levels, for the ACcoefficients by using Q1 instead of Q3, results in almost 17Kbit/sec saving in the sum-rate,however, at the expense of 2dB loss in PSNR. Similarly in Fig. 4, using Q1 instead of Q4results in almost 50Kbit/sec saving in the sum-rate. Second, the previous observationexplains the significantly improved performance of the practical system using MT-SCcompared to that using only JPEG for low-to-medium reconstructed image quality. Forexample, in Figures 3 and 4 the practical MT-SC achieves almost 2 and 3dB increase inPSNR at R≈85.543Kbit/sec, respectively, compared to that using only JPEG. The loss ofpractical system due to practical SW coding is less than 50Kbit/sec. Thirdly, theperformance of the proposed MT-SC system using CC sequences under asynchronoustransmission and fading channels is highly robust; the system demonstrates a minimal lossin the sum-rate with increased decoder speed. This loss is due to the worse fading channelcondition that requires increasing the bit rate to recover information. Rate control isadjusted based on puncturing rate of the turbo code. For example, in Fig. 3, at PSNR=31.5dB, the system achieves a BER less than 10−5 at relative speed of 30 and 120 km/h,

0 50 100 150 200 250 300 350 40027

28

29

30

31

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34

Sum rate [Kbit/sec]

PSN

R [

dB]

H264 / MVC (Multi-view)JPEG2000JPEG MT-SC with Ideal SW codingPractical MT-SC system Practical MT-SC system (3km/h)Practical MT-SC system (30km/h)Practical MT-SC system (120km/h)

Q2

Q1

Q4

Q6

Fig. 4 PSNR averaged over the two views, vs. total sum-rate R for the proposed system for AWGN and flat-fading channels assuming decoder speeds of 3, 30 and 120 km/h. The “Cone” stereo image of size 288-by-720 is used

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with an increase in R≈14.42 and 28.84Kbit/sec, respectively. Fourth, in both Figs. 3 and 4,the state-of-the art encoders JPEG2000 and H.264/MVC outperform the proposed systemby almost 3 and 6 dB, respectively. This comes at the expense of the increased complexityusing JPEG2000 and the strict need for communications between cameras in the case ofH.264/MVC encoder.

Figure 5 shows the output of our decoder for image “Cone” compared to that usingJPEG. As observed, the proposed decoder outperforms the JPEG decoder; with almost 2dBincrease in image perceptually using the proposed system at R≈85.543Kbit/sec compared tothe system using standard JPEG.

6 Conclusions

In this paper we propose a two-camera system for stereo image delivery over asynchronousand multipath fading channels and at variable decoder speeds of 3, 30 and 120 km/h. Thesystem combines MT-SC and CC spreading sequences to enable multiple accesscommunications.

The proposed system demonstrates improvement in reconstructed image qualitycompared to that of the JPEG systems, which is still traditionally used for low-complexity applications with PSNR gains varying between approximately 3dB to 0.5dBfor low to medium transmission rates, respectively. Though the used stereo images arecorrelated, gains over JPEG are minimal for the high PSNR due to the fact that increasingthe reconstructed image quality will require increasing the number of quantization levels/values. However, after quantization, only the most significant bit planes of the AC bands ofthe stereo images have the highest compression factor due to their high correlation, whilethe correlation between consecutive bit planes decreases as the bit plane significance leveldecreases and, therefore, the higher the number of bit planes in each DCT bands the lowerare the compression ratios achieved [3]. Similar gains of DVC and WZ coding over videostandards are reported in [9].

The proposed system demonstrates worse performance than the state-of-the art systemJPEG2000 and the multiview H.246/MVC. However, the use of the complex wavelet

a) JPEG PSNR = 27.2dB, 85.543 (Kbit/sec)

b) Pratical System PSNR = 29.1dB, 85.543 (Kbit/sec)

c) JPEG PSNR = 30.1dB, 128.713 (Kbit/sec)

d) Pratical System PSNR = 31dB, 128.713 (Kbit/sec)

Fig. 5 Reconstructed right view for the “Cone” stereo image using the proposed system (images b) and d))and the standard JPEG (images a) and c))

466 Multimed Tools Appl (2010) 48:457–470

technology in JPEG2000, will invariably require more power, memory and significantlyreduce the battery life in systems using low-complexity distributed image/video coding. Onthe other hand, the proposed system employs the less complex integer DCT and CCspreading/despreading that require no complex mathematical operations (e.g., mostly addsand shifts and limited multiplies) and this makes the proposed system a perfect candidatefor low complexity image coding applications.

Additionally, the proposed system, and in contrast to the H.264/MVC, requires nocommunications among cameras.

Finally, the key problem lies in the fact that views obtained from multiple cameras arenot correlated enough in the bit domain for the employed turbo codes that perform wellonly for particular channel models and high codeword sizes. Thus, a lot of work is stillneeded to further improve DSC-based designs. Our future work will focus on improving thecorrelation at the decoder side between the compressed streams.

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Dr Chadi Khirallah is currently a Research Fellow at the University of Strathclyde. Before joiningStrathclyde, Dr Khirallah held a postdoctoral position in Rostock University (Germany). He received hisMSc in Communication Engineering, funded by the British Chevening Scholarship, and PhD in Space-timecoding applications for advanced mobile communication networks, funded by NEC Europe Ltd., fromUniversity of Manchester Institute of Science and Technology (UMIST), and Lancaster University in 1999and 2006, respectively. Dr Khirallah published over 34 research papers in peer-reviewed internationaljournals and conference proceedings in the area of distributed image/video coding, scalable video coding,multimedia multicast, and mobile communications. He has been a technical program committee member ofEusipco-2009, and reviewer for IET, Springer and IEEE journals. In his current research project, entitled“wireless video transmission based on distributed source coding principles” (EPSRC 03/2007-02/2010), DrKhirallah has focused on developing practical code designs for application of distributed source coding(DSC) to real-time video multicast, and provided a DSC design for video delivery over heterogeneouswireless-wireline networks using turbo codes and complete complementary (CC) spreading codes. DrKhirallah research interests are DSP-enabled wireless communications including MIMO, error protectioncoding, network code design, and cooperative wireless transmission.

Vladimir Stanković is currently a Glasgow Research Partnership (GRP) Lecturer at the University ofStrathclyde. Before joining Strathclyde, Dr Stanković was with Texas A&M University as a ResearchAssistant Professor, and a Lecturer at Lancaster University. He obtained his Dipl-Ing (MEng) in ElectricalEngineering at University of Belgrade in 2000, and Dr-Ing (PhD) at University of Leipzig in 2003. Over thepast six years, he has published over 80 research papers in peer-reviewed leading scientific journals,prestigious international conference proceedings, and book chapters; he is the holder of UK and EU fundedresearch grants in the area of distributed compression. He has been awarded several patents in the area ofdistributed compression, signal processing and communications. He serves as an Associate Editor of IEEECommunications Letters. Dr Stanković has recently presented tutorials at major international conferences:

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IEEE ICC-2007, Eusipco-2008, ICCCN-2008, and IEEE ICASSP-2009. He has been a technical programcommittee member of international conferences and is a member of the organizing committee of Eusipco-2009, as Tutorial Chair. His research focuses on image/video processing, network information theory,wireless communications, and wireless ad hoc/sensor networks.

Lina Stanković (Fagoonee) is currently a Glasgow Research Partnership (GRP) Lecturer at the University ofStrathclyde. Before joining Strathclyde, she was at Lancaster University’s Dept. of Communication Systemsas a PhD student, research associate and then lecturer. She obtained her BEng (Hons) in ElectronicCommunications Engineering and PhD at Lancaster University in 1999 and 2003, respectively. She haspublished over 50 research papers in peer-reviewed international journals, conference proceedings, and bookchapters. She has worked with QinetiQ Malvern UK, BT Labs Martlesham UK, and Philips ResearchEindhoven, The Netherlands in the areas of signal processing and communications. Dr Stanković hasrecently presented tutorials at major international conferences: IEEE ICC-2007, Eusipco-2008, ICCCN-2008,and IEEE ICASSP-2009, on wireless media distribution. She has been a technical program committeemember of international conferences and is a member of the organizing committee of Eusipco-2009, asTutorial Chair. Her research interests are DSP-enabled wireless communications including MIMO, errorprotection coding, network code design, and cooperative wireless transmission.

Dr Samuel Cheng received the B.S. degree in Electrical and Electronic Engineering from the University ofHong Kong in 1995, and the M.Phil. degree in Physics and the M.S. degree in Electrical Engineering fromHong Kong University of Science and Technology and the University of Hawaii, Honolulu, in 1997 and2000, respectively. He received the Ph.D. degree in Electrical Engineering from Texas A&M University,College Station, in 2004. In 2004, he joined Advanced Digital Imaging Research, Houston, Texas. Since

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2006, he has been with the Department of Electrical and Computer Engineering at the University ofOklahoma, where he is currently an Assistant Professor. Dr. Cheng worked in Microsoft Asia, China, andPanasonic Technologies Company, New Jersey, in the areas of texture compression and digital watermarkingduring the summers of 2000 and 2001. He has been awarded three US patents, and is the co-recipient of the2006 IEEE Signal Processing Magazine Best Paper Award. Dr Cheng has recently presented tutorials atmajor international conferences: IEEE ICC-2007, Eusipco-2008, ICCCN-2008, and IEEE ICASSP-2009. Hisresearch interests include information theory, image/signal processing, and pattern recognition.

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