Facial Image Compression Based on Structured Codebooks ... - CVML

Hindawi Publishing CorporationEURASIP Journal on Applied Signal ProcessingVolume 2006, Article ID 69042, Pages 1–11DOI 10.1155/ASP/2006/69042<strong>Facial</strong> <strong>Image</strong> <strong>Compression</strong> <strong>Based</strong> on StructuredCodebooks in Overcomplete DomainJ. E. Vila-Forcén, S. Voloshynovskiy, O. Koval, and T. PunStochastic <strong>Image</strong> Processing Group, CUI, University of Geneva, 24 rue du Général-Dufour, Geneva 1211, SwitzerlandReceived 31 July 2004; Revised 16 June 2005; Accepted 27 June 2005We advocate facial image compression technique in the scope of distributed source coding framework. The novelty of the proposedapproach is twofold: image compression is considered from the position of source coding with side information and, contrarilyto the existing scenarios where the side information is given explicitly; the side information is created based on a deterministicapproximation of the local image features. We consider an image in the overcomplete transform domain as a realization of arandom source with a structured codebook of symbols where each symbol represents a particular edge shape. Due to the partialavailability of the side information at both encoder and decoder, we treat our problem as a modification of the Berger-Flynn-Grayproblem and investigate a possible gain over the solutions when side information is either unavailable or available at the decoder.Finally, the paper presents a practical image compression algorithm for facial images based on our concept that demonstrates thesuperior performance in the very-low-bit-rate regime.Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.1. INTRODUCTIONThe urgent demand of efficient image representation is recognizedby the industry and research community. Its necessityis highly increased due to the novel requirements of manyauthentication documents such as passports, ID cards, andvisas as well as recent extended functionalities of wirelesscommunication devices. The document, ticket, or even entrypass personalization are often requested in many authenticationor identification protocols. In most cases, classicalcompression techniques developed for generic applicationsare not suitable for these purposes.Wavelet-based [1, 2] lossy image compression techniques[3–6] have proved to be the most efficient from the ratedistortionpoint of view for the rate range of 0.2–1 bits perpixel (bpp). The superior performance of this class of algorithmsis justified by both decorrelation and energy compactionproperties of the wavelet transform and by the efficientadaptive both interband (zero trees [5]) and intraband(estimation quantization (EQ) [7, 8]) models that describethe data in the wavelet subbands. Recent results in waveletbasedimage compression show that some modest performanceimprovement (in terms of peak signal-to-noise ratio(PSNR) up to 0.3 dB) could be achieved either taking intoaccount the nonorthogonality of the transform [9] or usingmore complex higher-order context models of wavelet coefficients[10].During years, a standard benchmark database of imagesfor wavelet-based compression algorithm evaluationwas used. It includes several 512 × 512 grayscale test images(like Lena, Barbara, Goldhill) and the verification was performedfor the rates 0.2–1 bpp. In some applications, whichinclude person authentication data like photo images or fingerprintimages, the operational conditions might be different.In this case, especially for strong compression (below0.15 bpp), the resulting image quality of the state-of-the-artalgorithms is not satisfactory enough (Figure 1). Therefore,for this kind of applications more advanced techniques areneeded to satisfy the fidelity constrains.In this paper, we address the problem of classical waveletbasedimage compression enhancement by using side informationwithin a framework of distributed coding of correlatedsources. Recently, it was practically shown that it ispossible to achieve a significant performance gain when theside information is available at the decoder, while the encoderhas no access to the side information [11]. Using the side informationfrom an auxiliary analog additive white Gaussiannoise (AWGN) channel in the form of a noisy copy of theinput image at the decoder, it was reported a PSNR enhancementin the range of 1–2 dB depending on the test imageand the compression rate. It could be noted that the performanceof this scheme strongly depends on the state of theauxiliary channel, which should be known in advance at theencoding stage. Moreover, it is assumed that the noisy copy

2 EURASIP Journal on Applied Signal ProcessingXEncoderi ∈{1, 2,...,2 NRX }Decoder̂XFigure 3: Lossy source coding system without side information.(a) (b) (c)Figure 1: (a) 256 × 256 8-bit test image Slava. Results of compressionwith rate 0.071 bits per pixel (bpp) using (b) JPEG2000 standardsoftware (PSNR is 25.09 dB) and (c) state-of-the-art EQ coder(PSNR is 26.36 dB).{X, Y}p{x, y}XYEncoder XEncoder YNR XNR YFigure 2: Slepian-Wolf coding.Jointdecoderof the original image should be directly available at the decoder.This situation is typical for the distributed coding inthe remote sensing applications or can be simulated as inthe case of analog and digital television simulcast [11]. Inthe case of single-source compression, the side informationis not directly available at the decoder.The main goal of this paper consists in the developmentof a concept of single-source compression within adistributed coding framework using virtually created side information.This concept is based on the accurate approximationof a source data using a structured codebook, whichis shared by the encoder and decoder, and the communicationof the residual approximation term within the classicalwavelet-based compression paradigm.The paper is organized as follows. In Section 2, fundamentalsof source coding with side information are presented.In Section 3, an approach for single-source distributedlossy coding is introduced. A practical algorithm fora very-low-bit-rate compression of passport photo images isdeveloped in Section 4. Section 5 contains the experimentalresults and Section 6 concludes the paper.Notation 1. Scalar random variables are denoted by capitalletters X, bold capital letters X denote vector random variables,letters x and x are reserved to denote the realization of scalarand vector random variables, respectively. The superscript N isused to denote N-length vectors x N = x = {x 1 , x 2 , ..., x N },where the ith element is denoted as x i . X ∼ p X (x) or X ∼ p(x)indicates that a random variable X is distributed according top X (x). The mathematical expectation of a random variableX ∼ p X (x) is denoted by E pX [X] or E[X]. H(X), H(X, Y),H(X | Y) denotetheentropyoftherandomvariableX, thejoint entropy of the random variables X and Y,andtheconditionalentropy of the random variable X given Y, respectively.By I(X; Y) and I(X; Y | Z),wedenotethemutualinformationbetween the random variables X and Y, and the conditionalmutual information between the random variables X and Ygiven the random variable Z,respectively.R X denotes the rate ofcommunications for the random variable X. Calligraphic fontX is used to indicate sets X ∈ X, and|X| indicates the cardinalityof a set. R + is used to represent the set of positive realnumbers.2. DISTRIBUTED CODING OF CORRELATED SOURCES2.1. Slepian-Wolf encodingAssume that it is necessary to encode two discrete-alphabetpair wisely independent and identically distributed (i.i.d.)random variables X and Y with joint distribution p XY (x, y) =∏ Nk=1p XkY k(x k , y k ). A Slepian-Wolf [12, 13] codeallowsperforminglossless encoding of X and Y individually using twoseparate encoders, and the decoding is performed jointly aspresented in Figure 2. Using a random binning argument, itwas shown that the efficiency of such a code is the same as inthe case when joint encoding is used. It means that the encoderbit rates pair (R X , R Y ) is achievable when the followingrelationships hold:R X ≥ H(X | Y),R Y ≥ H(Y | X),R X + R Y ≥ H(X, Y).2.2. Lossy compression with side informationIn the lossy compression setup, it is necessary to achieve theminimal possible distortions for a given target coding rate.Depending on the availability of side information, severalpossible scenarios exist [14].No side information is availableImagine that it is needed to represent an i.i.d. source sequenceX ∼ p X (x), X ∈ X N using the encoding mappingf E : X N →{1, 2, ...,2 NRX } and the decoding mapping f D :{1, 2, ...,2 NRX }→X N with the minimum average bit rate Rbits per element. The fidelity of representation is evaluatedusing the average distortion D = (1/N) ∑ Nk=1 E[d(x k , ̂x k )],where the distortion measure d(x, ̂x) is determined in generalas a mapping X N × ̂X N → R + . Due to Shannon [12, 15], itis well known that the optimal performance of such a compressionsystem (Figure 3) (the minimal achievable rate forcertain distortion level) is determined by the rate-distortionfunction,R X (D) =(1)min I( X; ̂X ) . (2)p(̂x|x): ∑̂x,x p(̂x|x)d(̂x,x)≤D

J. E. Vila-Forcén et al. 3{X, Y}XEncoder XNR XXEncoder XNR X{X, Y}JointJointlem, that is, R X (0) WZX|Y = H(X | Y). rate regime.p{x, y}decoderp{x, y}decoderYYFigure 4: Wyner-Ziv coding.Side information is available only at the encoderIn this case, the performance limits coincide with the previouscase and the rate-distortion function could be determinedusing (2)[16].Figure 5: Berger-Flynn-Gray coding.Lossy compression of correlated sources(Berger-Flynn-Gray coding)This problem was investigated by Berger [18] and Flynn andGray [19], and the general scheme is presented in Figure 5.As in the previous case, Berger-Flynn-Gray coding refersSide information is available only at the decoderto the sequence of pairs {X, Y} ∼p(x, y), (X, Y) ∈ X N ×Y N ,(Wyner-Ziv coding)where now Y is available at both encoder and decoder, whilein the Wyner-Ziv problem it was available only at the decoder.Fundamental performance limits of source coding systemswith side information available only at the decoder (Figure 4)were established by Wyner and Ziv [12, 17]. The Wyner-Zivproblem could be formulated in the following way: given theIt is necessary to construct an R X -bits-per-elementjoint coder f E : X N × Y N →{1, 2, ...,2 NRX } and a joint decoderf D : {1, 2, ...,2 NRX }×Y N → ̂X N such that the averagedistortion satisfies E[d(X, f D (Y, f E (X, Y)))] ≤ D. In thisside information only at the decoder, what will be the minimumrate R X necessary to reconstruct the source X with avtionalrate-distortion function,case, the performance limits are determined by the condieragedistortion less than or equal to a given distortion valueD? By other words, assume that we have a sequence of independentdrawings of pairs {X k , Y k } of dependent randomR X (D) BFGX|Y = min ̂X; X | Y ) ,p(̂x|x,y)(6)variables, {X, Y} ∼p(x, y), (X, Y) ∈ X N × Y N .Ourgoalis to construct an R X -bits-per-element encoder f E : X N →{1, 2, ...,2 NRX } and joint decoder f D : {1, 2, ...,2where the minimization is performed over all p(̂x | x, y)subjectto the fidelity constraint (3). The Berger-Flynn-Gray rateNRX }×Y N → ̂X N such that the average distortion satisfies the fidelityconstraint:in (6) is, in general, smaller than the Wyner-Ziv rate (5) sincethe availability of the correlated source Y at both encoder andE [ d ( (X, f D Y, fE (X) ))] = ∑ p(x, y)p (̂x | x, y ) ≤ D.x,̂x(3)decoder makes possible to reduce the ambiguity about X.Comparing the rate-distortion performance of differentcoding scenarios with the side information, it should beUsing the asymptotic properties of random codes, it wasnoted that, in general, the following inequalities hold [20]:shown [17] that the set of achievable rate-distortion pairs ofsuch a coding system will be bounded by the Wyner-Ziv ratedistortionR X (D) ≥ R X (D) WZX|Y ≥ R X(D) BFGX|Y . (7)function:R X (D) WZX|Y = min [ ( ] The last inequality becomes equality, that is, R X (D) WZX|Y =I U; X) − I(U; Y) , (4)p(u|x)p(̂x|x,y)R X (D) BFGX|Y , only for the case of Gaussian distribution of thesource X and mean square error (MSE) distortion measure.where the minimization is performed over all p(u | x)p(̂x |x, y) and all decoder functions f D satisfying the fidelity constraint(3). U is an auxiliary random variable such that |U| ≤For any other pdf, performance loss exists in the Wyner-Zivcoding. It was shown in [20] that this loss is upper boundedby 0.5 bit,|X| +1andY → X → U forms a Markov chain. Hence, (4)could be rewritten as follows:R X (D) WZX|Y − R X(D) BFGX|Y ≥ 0.5. (8)R X (D) WZX|Y = min I(U; X | Y),p(u|x)p(̂x|x,y)(5)Therefore, due to the fact that natural images have highlynon-Gaussian statistics [8, 21, 22], compression of this datawhere the minimization is performed over all p(u | x)p(̂x |x, y) subject to the fidelity constraint (3).It is worth to note that for the case of zero distortions, theWyner-Ziv problem corresponds to the Slepian-Wolf prob-using the Wyner-Ziv strategy will always lead to the performanceloss. The main goal of subsequent sections consists inthe extension of the classical distributed coding setup to thecase of a single-source coding scenario in the very-low-bit-

4 EURASIP Journal on Applied Signal ProcessingXiMain encoderTransition detection Y Index encoderShape codebookjDecoderFigure 6: Block diagram of single-source distributed coding withside information.̂XShape index j··· ··· ··· ··· ···y 1 (1) y 2 (1) ... y J (1) y 1 (i) y 2 (i) ... y J (i) y 1 (M) y 2 (M) ... y J (M)Shape coset 1 Shape coset i Shape coset MFigure 8: Shape cosets from the shape codebook Y.(a)Figure 7: (a) Test image Slava and its fragment (marked by square):two-region modeling of the fragment, (b) in the coordinate domain,and (c) in the nondecimated wavelet transform domain.3. PRACTICAL APPROACH: DISTRIBUTED SOURCECODING OF A SINGLE SOURCE3.1. Coding block diagramThe block diagram of a practical single-source distributedcoding system with side information is presented in Figure 6.The system consists of two main functional parts. The firstpart includes the main encoder that is working as a classicalquantization-based lossy coder with varying rates. The secondpart includes the block of transition detection that approximatesthe image edges and creates some auxiliary imageY, as a close approximation to X. The index encoder communicatesthe parameters of approximation model to the decoder.The shape codebook is shared by both transition detectionblock and decoder.The intuition behind our approach is based on the assumptionthat natural images in the coordinate domain canbe represented as a union of several stationary regions of differentintensity levels or in the nondecimated wavelet transformdomain [23] using edge process (EP) model. This assumptionand the EP model have been used in our previouswork in image denoising where promising results have beenreported [24].Under the EP model, an image in the coordinate domain(Figure 7(a)) is composed of a number of nonoverlappingsmooth regions (Figure 7(b)). Accordingly, in the criticallysampled or nondecimated wavelet transform domain, it isrepresented as a union of two types of subsets: the first onecontains all samples from flat image areas, while the second(b)(c)one represents edges and textures. It is supposed that thesamples from the latter subset propagate along the transitiondirection (Figure 7(c)). Accurate tracking of the region separationboundary in the coordinate domain setup or transitionprofile propagation in the transform domain setup allowedto achieve image denoising results that are among thestate-of-the-art for the case of AWGN [24].3.2. Codebook constructionContrarily to the image denoising setup, in the case of lossywavelet-based image compression we are interested in consideringnot the behavior of edge profile along the directionof edge propagation, but the different edge profiles. Due tothe high variability of edge shapes in real images and thecorresponding complexity of the approximation problem, wewill exploit a structured codebook for shape representation.It means that several types of shapes will be used to constructa codebook where each codeword represents one edgeof some magnitude. A schematic example of such a codebookis given in Figure 8, where several different edge profilesare exploited for image approximation. This structured codebookhas a coset-based structure, where each coset containsthe selected triple of edge profiles of a certain amplitude.More formally, the structured codebook Y = {y(i)},where i = 1, 2, ..., M, andacoset(9) canberepresentedasin Figure 9:⎧y1(i) 1 y2(i) 1 ··· y 1 ⎫N(i)⎪⎨ y1(i) 2 y2(i) 2 ··· yN(i)2 ⎪⎬y(i) =... ... ... (9)⎪⎩y1(i) J y2(i) J ··· yN(i)J ⎪⎭Here, y j (i) represents the shape j from the shape coset i. Allshape cosets i consist of the same shape profiles, that is, j ∈{1, 2, ..., J},andi ∈{1, 2, ..., M} for the example presentedin Figure 8.Important points about the codebook are as follows: (a)it is image independent, (b) the considered shapes are unidimensional,(c) the codewords shape could be expressed analytically,for instance, using apparatus of splines, and (d) thecodebook dimensionality is determined by the type of transformused and the compression regime. Therefore, a conceptof successive construction refinement [25] of the codebook

J. E. Vila-Forcén et al. 5⎡ ⎡y1(1) 1 y2(1) 1 ··· y 1 ⎤ ⎤N(1)y 1(1) 2 y2(1) 2 ··· y 2 N(1)⎢⎣ ... ... ..⎥⎦y 1(1) J y2(1) J ··· y J N(1).⎡y 1(i) 1 y2(i) 1 ··· y 1 ⎤N(i)y 1(i) 2 y2(i) 2 ··· y 2 N(i)⎢⎣ ... ... ..⎥⎦y J 1(i) y2(i) J ··· yN(i)J .⎡y 1(M) 1 y2(M) 1 ··· yN(M)1 ⎤y 2 1(M) y2(M) 2 ··· yN(M)2 ⎢⎣⎢⎣ ... ... ..⎥⎥⎦⎦y1(M) J y2(M) J ··· yN(M)JShape coset 1Shape coset iShape coset MFigure 9: Structured codebook: shape coset index i (or magnitude)is communicated explicitly by the main encoder as transition locationand magnitude of quantized coefficients, and shape index j(j ∈{1, 2, ..., J}) is encoded by index encoder.might be used. The intuition behind this approach could beexplained using the coarse-fine quantization framework presentedin Figure 10.It means that for the case of high compression ratios,when there is not much rate to code the shape index, a singleshape profile will be used (like a coarse quantizer). In otherregimes (at medium or at high rates), it is possible to improvethe fidelity of approximation adding more edge shapes to thecodebook. In this case, we could assume that the high-ratequantization assumption becomes valid.The task of real edge approximation according to theshape codebook can be formulated, for instance, like a classicall 2 norm approximation problem,ỹ j (i) =argmin{y j (i)},1≤i≤M,1≤j≤J∥ x − y j (i) ∥ ∥ 2 , (10)where the minimization is performed over the whole codebookin each image point.3.3. Practical implementation: high-, medium-,and low-bit-rate regimesIt is clear that in the presented setup, the computational complexityof image approximation in each point will be significant,and can be unacceptable in some realtime applicationscenarios. To simplify the situation, searching space dimensionalitymight be significantly reduced using techniques thatsimplify the edge localization. Canny edge detector [26] canbe used for this purpose.The edge of a real image could be considered as anoisy or distorted version of the corresponding codeword{y j 1(i), y j 2(i), ..., y j N(i)} (edge shape) with respect to thecodebook Y, that is, some correlation between an originalCompr. ratioShape coset iCoarsecodebookFinecodebookFigure 10: Successive refinement codebook construction.edge and a codeword can be assumed. Therefore, the structureof the codebook is similar to the structure of a channelcoset code [27], meaning that the distance between codewordsofequalmagnitude(Figure8) in the transform domainshould be large enough to perform correct shape approximation.The coding strategy can be performed in a distributedmanner. In general, the main encoder performs the quantizationof the edge and communicates the corresponding indicesof reconstruction levels to the decoder. This informationis sufficient to determine the shape coset index i at thedecoder for different compression regimes, including evenvery-low-bit-rate regime (besides the case when quantizationto zero is performed). The index j of edge shape withina coset is communicated by the index encoder to the decoder.Having the coset index and the shape index, the decoderlooks in the coset bin i for y j (i) and generates the reproductionsequence ̂x = f D (̂x ′ (i), ỹ j (i)), where ̂x ′ (i) is thedata reproduced at the decoder based only on the index i.In the case of high rates, the main encoder performs ahigh-rate (high-accuracy) approximation of the image edges.It means that the index encoder does not produce any output,that is, both edge magnitude and edge shape could be reconstructeddirectly from the information contained in the maindecoder bit stream. Therefore, the role of side informationrepresented by the fine codebook consists in the compensationof quantization noise influence.For middle rates, the edge magnitude prediction is stillpossible using the main encoder bitstream. However, theedge shape approximation accuracy for this regime is nothigh enough to estimate the edge shape and its index shouldbe communicated to the decoder by the index encoder. Onecan note that in such a way we end up with vector-like edgequantization using the off-line designed edge codebook. Therole of the side information remains similar to the previouscase and targets the compensation of quantization error.At low rates, a single codeword (optimal in the meansquare error sense) should be chosen to represent all shapeswithin the given image (coarse codebook in Figure 10). Inmore general case, one can choose a single shape codewordthat is the same for all images. This is a valid assumption forthe compression of image databases with the same type ofimages. Contrarily to the above case of middle rates, the decoderoperates with a single edge codeword that can be appliedto all cases where the edge coefficients are partially preservedin the corresponding subbands. Moreover, the edge

6 EURASIP Journal on Applied Signal Processingreconstruction is possible even when the edge coefficientsin some subbands are completely discarded by the deadzonequantization.The practical aspects of implementation of the presentedsingle source coding system with side information are outof the scope of the paper. In the following section, we willpresent an application of the proposed framework to thevery-low-bit-rate compression of passport photo images.4. DISTRIBUTED CODING OF IMAGES WITHSYMMETRIC SIDE INFORMATION:COMPRESSION OF PASSPORT PHOTOSAT VERY LOW BIT RATES141062−2−6−10−14Zero-crossing pointIn this section, the case of single source distributed codingsystem with side information is discussed for the case ofvery-low-bit-rate (less than 0.1 bpp) compression of passportphoto images. The importance of this task is justified by theurgent necessity to store personal information on the capacityrestricted media authentication documents that includepassports, visas, ID cards, driver licenses, and credit cards usingdigital watermarks, barcodes, or magnetic strips. In thispaper, we assume that the images of interest are 8-bit grayscale images of 256 × 256 size. As it was shown in Figure 1,existing compression tools are unable to provide the satisfactoryquality solution to this task.The scheme presented in Figure 6 is used as a basic setupfor this application. As it was discussed earlier, for the caseof very-low-bit-rate regime, only one shape profile (simplestep edge) is exploited. Therefore, index encoder is not usedin this particular case since only one index is possible as itsoutput and, therefore, it is known a priory by the decoder.Certainly, better performance can be expected if one approximatestransitions using complete image codeword (Figure 6).The price to pay for that is additional log 2 J bits of side informationper shape, where J is the number of edge shapeswithin each coset.In the next subsections, we discuss in details the particularitiesof encoding and decoding at the very-low-bit rates.4.1. Transition detectionEncodingDue to the fact that high-contrast edges consume a significantamount of the allocated bit budget for the complete imagestorage, it would be beneficial from the reconstructed imagequality perspective to reduce the ambiguity about theseimage features.On the first step, the position of the principal edges (theedges with the highest contrast) is detected using the Cannyedge detector. Due to the fact that the detection result isnot always precise (some position deviation is possible), actualtransition location is detected using the zero-crossingconcept.Zero-crossing concept is based on the fact that in the nondecimatedwavelet transform domain (algorithm atrois[23]1st subband2nd subband3rd subband4th subbandFigure 11: Zero-crossing concept: 4-level decomposition of the stepedge in the nondecimated domain.is used for its implementation) all the representations of anideal step edge from different decomposition levels in thesame spatial orientation cross the horizontal axis in the samepoint referred to as the zero-crossing point (Figure 11). Thispoint coincides with a spatial position of the transition inthe coordinate domain. Besides, the magnitudes of principlepeaks (maximum and minimum values of the data inthe vicinity of transition in the nondecimated domain) ofthe components are related pairwise from high to low frequencieswith certain fixed ratios which are known in advance.Therefore, when the position of the zero-crossing pointis given and, at least, one of the component peak magnitudesis known from original step edge, it is possible to predict andto reconstruct the missing data components with no error.Consequently, if it is known at the decoder that an idealstep edge with a given amplitude is presented in a given imagelocation, it is possible to assign zero rate to the predictablecoefficients at the encoder, allowing higher quality reconstructionof unpredictable information.DecodingFor this mode, it is assumed that the low-resolution versionof the original data obtained using main encoder bitstreamis already available. Detection of the coarse positions of mainedges is performed on the interpolated image analogically tothe encoder case. To adjust detection results, a new conceptof zero-crossing detection is used.In the targeted very-low-bit-rate compression scenario,the data are severely degraded by quantization. To make zerocrossingdetection more reliable in this case, more levels ofthe nondecimated wavelet transform can be used. The additionalreliability is coming from the fact that the data atthe very low-frequency subbands almost do not suffer fromquantization. The gain in this case is limited by the informationthat is still presented at these low frequencies: only the

J. E. Vila-Forcén et al. 7210−1−2Maximum (3rd)Maximum (4th)Zero-crossing point3rd subband4th subbandFigure 12: Zero-crossing concept: decoding stage.edges propagating in all the subbands could be detected insuch a way.In order to reconstruct high-frequency subbands, bothedge position and edge magnitude are predicted usinglow-frequency subbands. In Figure 12, the position of thezero-crossing point is estimated based on the data from the3rd and 4th subbands. Having their maximum magnitudevalues, the reconstruction of high frequency subbands canbe performed accurately based on the fixed magnitude relationships(Figure 11).4.2. Main encoderTo justify the main encoder structure, we would like to pointout that the main gain achieved recently in wavelet-basedlossy transform image coding is due to the accuracy of theunderlying stochastic image model.One of the most efficient and accurate stochastic imagemodels that represent images in the wavelet transformdomain is based on the parallel splitting of the Laplaciansource firstly introduced by Hjorungnes et al. [28]. The mainunderlying assumption here is that global i.i.d. zero-meanLaplacian data can be represented, without loss according tothe Kullback-Leibler divergence, using an infinite mixture ofGaussian pdfs with zero-mean and exponentially distributedvariances,λ2 e(−λ|x|) =∫ ∞01√ 2πσ 2 e(x2 /2σ 2) λe (−λσ2) dσ 2 , (11)where λ is the parameter of the Laplacian distribution. TheLaplacian distribution is often used to model the globalstatistics of the high-frequency wavelet coefficients [8, 21,22].Hjorungnes et al. [28] were the first who demonstratedthat, if the side information (the local variances) are availableat both encoder and decoder, the gain in the rate-distortionsense of coding the Gaussian mixture instead of the globalLaplacian source is given byR L (D) − R MG (D) ≈ 0.312 bit/sample, (12)where R L (D) andR MG (D) denote the rate-distortion functionsfor the global i.i.d. Laplacian source and the Gaussianmixture, respectively.The practical problem of the side information communicationto the decoder was elegantly solved in [7, 8]. Thedeveloped EQ coder is based on the assumption of the slowvarying nature of the local variances of the high-frequencysubband image samples. As a consequence, this variance canbe accurately estimated (predicted) given its quantized causalneighborhood.According to the EQ coding strategy, the local variancesof the samples in the high-frequency wavelet subbands are estimatedbased on the causal neighborhood using maximumlikelihood strategy. When it is available, the data from theparent subband are also included to enhance the estimationaccuracy.At the end of the estimation step, the coefficients arequantized using a uniform threshold quantizer selected accordinglyto the results of the rate-distortion optimization.In particular, the Lagrange functional should be minimized,that on the sample level is given byy i = r i + λd i , (13)where r i is the rate corresponding to the entropy of the quantizeroutput applied to the ith sample, d i is the correspondingdistortion, and λ is the Lagrange multiplier. The encoding ofthe quantized data is performed using the bin probabilitiesof the quantizers, where the samples fall, by an arithmeticcoder.While at the high-rate regime the approximation of thelocal variance field by its quantized version is valid, in thecase of low rates it fails. The reason for that is the quantizationto zero most of the data samples that makes local varianceestimation extremely inaccurate.The simple solution proposed in [7, 8] consists in theplacement of all the coefficients that fall into the quantizerdeadzone in the so-called unpredictable class, and the rest inthe so-called predictable class. The samples of the first oneare considered to be distributed globally as an i.i.d. generalizedGaussian distribution, while the infinite Gaussian mixturemodel is used to capture the statistics of the samples inthe second one. This separation is performed using a simplerate-dependent thresholding operation. The parametersof the unpredictable class are exploited in the rate-distortionoptimization and are sent to the decoder as side information.The experimental results presented in [7, 8]allowtoconcludeabout the state-of-the-art performance of this techniquein the image compression application.

8 EURASIP Journal on Applied Signal ProcessingTable 1: Benchmarking of the developed compression method versus existing lossy encoding techniques.Bytes(bpp)300(0.037)400(0.049)500(0.061)600(0.073)700(0.085)800(0.099)Slava (PSNR, dB) Julien (PSNR, dB) Jose (PSNR, dB)ROIROIROIROIROIROIJPEG EQ DSSCEQ DSSCSPIHTJPEG EQSPIHTJPEGSPIHT2000 2000 200021.04 25.27 10.33 25.93 19.87 22.82 9.76 23.35 20.76 26.11 10.39 27.2922.77 26.08 18.56 26.81 21.47 23.43 17.94 23.89 23.21 27.30 19.86 28.2724.92 26.85 22.36 27.41 23.07 23.81 21.86 24.15 25.50 28.20 25.09 28.8125.78 27.41 25.96 27.85 23.78 24.17 22.81 24.28 26.39 28.74 27.09 29.1026.66 27.96 27.12 28.09 24.53 24.44 23.61 24.50 28.08 29.31 28.37 29.3527.39 28.56 27.71 28.16 25.04 24.68 24.24 24.56 28.72 29.89 29.31 29.46DSSCposition of the rectangular region-of-interest, and four bytescharacterizing the background brightness.(a)Figure 13: Test image Slava: (a) region of interest and (b) backgroundfour-quadrant splitting.Motivated by the EQ coder performance, we designedour main encoder using the same principles with severalmodifications as follows:(i) at the very-low-bit-rate regime, most of the informationat the first and the second wavelet decompositionlevels is quantized to zero. We assume that all the dataabout strong edges could be reconstructed with someprecision using the side information and do not allocateany rate to these subbands;(ii) high-frequency subbands of the third decompositionlevel are compressed using a region of interest strategy(Figure 13(a)), where the region of interest is indicatedusing three extra bytes. The image regions outside ofthe region of interest will be reconstructed using lowfrequencyinformation, and four extra bytes for themean brightness of the background of the photo imagein four quadrants (Figure 13(b));(iii) a 3 × 3 causal window is applied for local variance estimation;(iv) no parent dependencies are taken into account on thestochastic image model, and only samples from thegiven subband are used [29].The actual bitstream from the encoder is constituted bythe data from the EQ encoder, three bytes determining the(b)4.3. Index encoderAs it was mentioned in the previous subsection, only oneedge profile (the step edge) is used at the very-low-rateregime. Thus, index encoder does not produce any output.4.4. DecoderThe decoder performs the reconstruction of the compresseddata using the main encoder output and the available sideinformation. The bitstream of the main encoder is decompressedby the EQ decoder. The fourth wavelet transform decompositionlevel is decompressed using classical algorithmversion, and the third level is reconstructed using region ofinterest EQ decoding.Having two lowpass levels of decomposition, the lowresolutionreconstruction (with two high-frequency decompositionlevels equal to zero) of the original photo usingwavelet transform is obtained. Final reconstruction of highqualitydata is performed based on the interpolated image,and the transition detection block information in the nondecimatedwavelet transform domain.5. EXPERIMENTAL RESULTSIn this section, we present the experimental results of verylow-bit-ratepassport photo compression based on the proposedframework of distributed single source coding withsymmetrical side information (DSSC). A set of 11 imageswere used in our experiments. The results for three of themare presented in Table 1, Figures 14 and 15 versus those providedby the standard EQ algorithm as well as JPEG2000 withregion of interest coding (ROI-JPEG2000) [30] and set partitioningin hierarchical trees algorithm with region of interestcoding (ROI-SPIHT) [31].

J. E. Vila-Forcén et al. 9292530272428PSNR (dB)2523PSNR (dB)2322PSNR (dB)2621300 400 500 600 700 800BytesROI-JPEG 2000ROI-SPIHTEQDSSC21300 400 500 600 700 800BytesROI-JPEG 2000ROI-SPIHTEQDSSC24300 400 500 600 700 800BytesROI-JPEG 2000ROI-SPIHTEQDSSC(a)(b)(c)Figure 14: Benchmarking of the developed compression method versus existing lossy encoding techniques: (a) Slava,(b)Julien, and (c) Josetest images.(1a) (1b) (1c) (1d) (1e) (1f) (1g) (1h) (1i)(2a) (2b) (2c) (2d) (2e) (2f) (2g) (2h) (2i)(3a) (3b) (3c) (3d) (3e) (3f) (3g) (3h) (3i)Figure 15: Experimental results. The first column: the original test images; the second column: ROI-JPEG2000 compression results for therate 400 bytes; the third column: ROI-SPIHT compression results for the rate 400 bytes; the fourth column: EQ compression results for therate 400 bytes; the fifth column: DSSC compression results for the rate 400 bytes; the sixth column: ROI-JPEG2000 compression results forthe rate 700 bytes; the seventh column: ROI-SPIHT compression results for the rate 700 bytes; the eighth column: EQ compression resultsfor the rate 700 bytes; and the ninth column: DSSC compression results for the rate 700 bytes.The performance is evaluated in terms of the peak signalto-noiseratio PSNR = 10 log 10 (255 2 /‖x − ̂x‖ 2 ).The obtained results allow to conclude about the proposedmethod advantages over the selected competitors forcompression rates below 0.09 bpp in terms of both visualquality and PSNR. Performance loss at higher rate in our casein comparison with ROI-SPIHT and ROI-JPEG2000 is explainedby the necessity of algorithm performance optimizationfor this rate regime that includes a modification of theunpredictable class definition.6. CONCLUSIONSIn this paper, the problem of distributed source coding of asingle source with side information was considered. It wasshown that the compression system optimal performancefor non-Gaussian sources can be achieved using the Berger-Flynn-Gray coding setup. A practical very-low-bit-rate compressionalgorithm based on this setup was proposed for codingof passport photo images. Experimental validation of thisalgorithm performed on a set of passport photos allows to

10 EURASIP Journal on Applied Signal Processingconclude its superiority over a number of existing encodingtechniques at rates below 0.09 bpp in terms of both visualquality and PSNR. The realized performance loss of thedeveloped algorithm at rates higher than 0.09 bpp is justifiedby the necessity of its parameters optimization for thisrate range. This extension is a subject of our ongoing research.DISCLAIMERThe information in this document reflects only the authors’views, is provided as is and no guarantee or warranty is giventhat the information is fit for any particular purpose. Theuser thereof uses the information at its sole risk and liability.ACKNOWLEDGMENTSThis paper was partially supported by SNF ProfessorshipGrant no. PP002-68653/1, Interactive Multimodal InformationManagement (IM2) project, and by the EuropeanCommission through the IST Programme under ContractIST-2002-507932 ECRYPT and FP6-507609-SIMILAR. Theauthors are thankful to the members of the Stochastic <strong>Image</strong>Processing Group at University of Geneva and to Pierre Vandergheynst(EPFL, Lausanne) for many helpful and interestingdiscussions. The authors also acknowledge the valuablecomments of the anonymous reviewers.REFERENCES[1] I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia,Pa, USA, 1992.[2] S. G. Mallat, “A theory for multiresolution signal decomposition:the wavelet representation,” IEEE Transactions on PatternAnalysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693,1989.[3] C. Chrysafis and A. Ortega, “Efficient context-based entropycoding lossy wavelet image compression,” in Proceedings ofData <strong>Compression</strong> Conference (DCC ’97), pp. 241–250, Snowbird,Utah, USA, March 1997.[4] A. Said and W. A. 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J. E. Vila-Forcén et al. 11IEEE International Conference on Acoustics, Speech, and SignalProcessing (ICASSP ’96), vol. 4, pp. 2343–2346, Atlanta, Ga,USA, May 1996.[26] J. Canny, “A computational approach to edge detection,”IEEE Transactions on Pattern Analysis and Machine Intelligence,vol. 8, no. 6, pp. 679–698, 1986.[27] J. G. Proakis, Digital Communications, McGraw-Hill,NewYork, NY, USA, 3rd edition, 1995.[28] A. Hjorungnes, J. M. Lervik, and T. A. Ramstad, “Entropycoding of composite sources modeled by infinite Gaussianmixture distributions,” in Proceedings of IEEE Digital SignalProcessing Workshop, pp. 235–238, Loen, Norway, September1996.[29] K. R. Rao and P. C. Yip, Eds., The Transform and Data <strong>Compression</strong>Handbook, CRC Press, Boca Raton, Fla, USA, 2000.[30] C. Christopoulos, J. Askelof, and M. Larsson, “Efficient methodsfor encoding regions of interest in the upcoming JPEG2000 still image coding standard,” IEEE Signal Processing Letters,vol. 7, no. 9, pp. 247–249, 2000.[31] E. Atsumi and N. Farvardin, “Lossy/lossless region-of-interestimage coding based on set partitioning in hierarchical trees,”in Proceedings of IEEE International Conference on <strong>Image</strong> Processing.(ICIP ’98), vol. 1, pp. 87–91, Chicago, Ill, USA, October1998.J. E. Vila-Forcén received the TelecommunicationsEngineer degree from the CarlosIII University of Madrid, Spain, in 2001. In2001–2002, he joined the Signal Theory andCommunications Department of the CarlosIII University, working in the developmentof the MPEG4 standard. Since 2002,he has been an Assistant Professor and aPh.D. student at the Stochastic <strong>Image</strong> ProcessingGroup, Computer Vision and MultimediaLab, Department of Computer Science of the Universityof Geneva, Geneva, Switzerland. His current research interests arethe information-theoretic aspects of digital data hiding, communicationswith side information, and stochastic image modeling forcompression.O. Koval received his M.S. Degree in electricalengineering from the National UniversityLvivska Politechnika, Lviv, Ukraine,in 1996. In 1996–2001, he was with theDepartment of Synthesis, Processing, andIdentification of <strong>Image</strong>s, Institute of Physicsand Mechanics (Lviv, Ukraine) as a researcherand Ph.D. student. He receivedhis Ph.D. degree in electrical engineeringfrom the National University Lvivska Politechnika,in 2002. Since 2002, he has been with Stochastic <strong>Image</strong>Processing Group, Computer Vision and Multimedia Lab, Universityof Geneva, from which he received his Ph.D. degree in stochasticimage modeling in 2004, where he is currently a PostdoctoralFellow. His research interests cover stochastic image modeling fordifferent image processing applications, digital watermarking, informationtheory, and communications with side information.T. Pun received his Ph.D. degree in imageprocessing in 1982, at the Swiss FederalInstitute of Technology in Lausanne(EPFL). He joined the University of Geneva,Switzerland, in 1986, where he is currently aFull Professor at the Computer Science Departmentand Head of the Computer Visionand Multimedia Lab. Since 1979, hehas been active in various domains of imageprocessing, image analysis, and computervision. He has authored or coauthored over 200 journal andconference papers in these areas as well as seven patents, and ledor participated to a number of national and European researchprojects. His current research interests, related to the design of multimediainformation systems and multimodal interaction, focuson data hiding, image and video watermarking, image and videocontent-based information retrieval systems, EEG signals analysis,and brain-computer interaction.S. Voloshynovskiy received the Radio Engineerdegree from Lviv Polytechnic Institutein 1993, and the Ph.D. degree inelectrical engineering from State UniversityLvivska Politechnika, Lviv, Ukraine, in 1996.In 1998–1999, he has been with Universityof Illinois at Urbana-Champaign, USA,as a Visiting Scholar. Since 1999, he hasbeen with University of Geneva, Switzerland,where he is currently an Associate Professorwith the Department of Computer Science, and Head of theStochastic <strong>Image</strong> Processing Group. His current research interestsare in information-theoretic aspects of digital data hiding, visualcommunications with side information, and stochastic image modelingfor denoising, compression, and restoration. He has coauthoredover 100 journal and conference papers in these areas as wellas nine patents. He has served as a consultant to private industry inthe above areas.

EURASIP JOURNAL ON APPLIED SIGNAL PROCESSINGSpecial Issue onTransforming Signal Processing Applications intoParallel ImplementationsCall for PapersThere is an increasing need to develop efficient “systemlevel”models, methods, and tools to support designers toquickly transform signal processing application specificationto heterogeneous hardware and software architectures suchas arrays of DSPs, heterogeneous platforms involving microprocessors,DSPs and FPGAs, and other evolving multiprocessorSoC architectures. Typically, the design process involvesaspects of application and architecture modeling aswell as transformations to translate the application modelsto architecture models for subsequent performance analysisand design space exploration. Accurate predictions are indispensablebecause next generation signal processing applications,for example, audio, video, and array signal processingimpose high throughput, real-time and energy constraintsthat can no longer be served by a single DSP.There are a number of key issues in transforming applicationmodels into parallel implementations that are not addressedin current approaches. These are engineering theapplication specification, transforming application specification,or representation of the architecture specification aswell as communication models such as data transfer and synchronizationprimitives in both models.The purpose of this call for papers is to address approachesthat include application transformations in the performance,analysis, and design space exploration efforts when takingsignal processing applications to concurrent and parallel implementations.The Guest Editors are soliciting contributionsin joint application and architecture space exploration thatoutperform the current architecture-only design space explorationmethods and tools.Topics of interest for this special issue include but are notlimited to:• modeling applications in terms of (abstract)control-dataflow graph, dataflow graph, and processnetwork models of computation (MoC)• transforming application models or algorithmicengineering• transforming application MoCs to architecture MoCs• joint application and architecture space exploration• joint application and architecture performanceanalysis• extending the concept of algorithmic engineering toarchitecture engineering• design cases and applications mapped onmultiprocessor, homogeneous, or heterogeneousSOCs, showing joint optimization of application andarchitectureAuthors should follow the EURASIP JASP manuscriptformat described at http://www.hindawi.com/journals/asp/.Prospective authors should submit an electronic copy oftheir complete manuscript through the EURASIP JASP manuscripttracking system at http://www.hindawi.com/mts/, accordingto the following timetable:Manuscript Due September 1, 2006Acceptance Notification January 1, 2007Final Manuscript Due April 1, 2007Publication Date 3rd Quarter 2007GUEST EDITORS:F. Deprettre, Leiden Embedded Research Center, LeidenUniversity, Niels Bohrweg 1, 2333 CA Leiden, TheNetherlands; edd@liacs.nlRoger Woods, School of Electrical and ElectronicEngineering, Queens University of Belfast, Ashby Building,Stranmillis Road, Belfast, BT9 5AH, UK; r.woods@qub.ac.ukIngrid Verbauwhede, Katholieke Universiteit Leuven,ESAT-COSIC, Kasteelpark Arenberg 10, 3001 Leuven,Belgium; Ingrid.verbauwhede@esat.kuleuven.beErwin de Kock, Philips Research, High Tech Campus 31,5656 AE Eindhoven, The Netherlands;erwin.de.kock@philips.comHindawi Publishing Corporationhttp://www.hindawi.com

EURASIP JOURNAL ON APPLIED SIGNAL PROCESSINGSpecial Issue onVideo Adaptation for Heterogeneous EnvironmentsCall for PapersThe explosive growth of compressed video streams andrepositories accessible worldwide, the recent addition of newvideo-related standards such as H.264/AVC, MPEG-7, andMPEG-21, and the ever-increasing prevalence of heterogeneous,video-enabled terminals such as computer, TV, mobilephones, and personal digital assistants have escalated theneed for efficient and effective techniques for adapting compressedvideos to better suit the different capabilities, constraints,and requirements of various transmission networks,applications, and end users. For instance, Universal MultimediaAccess (UMA) advocates the provision and adaptation ofthe same multimedia content for different networks, terminals,and user preferences.Video adaptation is an emerging field that offers a richbody of knowledge and techniques for handling the hugevariation of resource constraints (e.g., bandwidth, display capability,processing speed, and power consumption) and thelarge diversity of user tasks in pervasive media applications.Considerable amounts of research and development activitiesin industry and academia have been devoted to answeringthe many challenges in making better use of video contentacross systems and applications of various kinds.Video adaptation may apply to individual or multiplevideo streams and may call for different means depending onthe objectives and requirements of adaptation. Transcoding,transmoding (cross-modality transcoding), scalable contentrepresentation, content abstraction and summarization arepopular means for video adaptation. In addition, video contentanalysis and understanding, including low-level featureanalysis and high-level semantics understanding, play an importantrole in video adaptation as essential video contentcan be better preserved.The aim of this special issue is to present state-of-theartdevelopments in this flourishing and important researchfield. Contributions in theoretical study, architecture design,performance analysis, complexity reduction, and real-worldapplications are all welcome.Topics of interest include (but are not limited to):• Heterogeneous video transcoding• Scalable video coding• Dynamic bitstream switching for video adaptation• Signal, structural, and semantic-level videoadaptation• Content analysis and understanding for videoadaptation• Video summarization and abstraction• Copyright protection for video adaptation• Crossmedia techniques for video adaptation• Testing, field trials, and applications of videoadaptation services• International standard activities for video adaptationAuthors should follow the EURASIP JASP manuscriptformat described at http://www.hindawi.com/journals/asp/.Prospective authors should submit an electronic copy oftheir complete manuscript through the EURASIP JASP manuscripttracking system at http://www.hindawi.com/mts/, accordingto the following timetable:Manuscript Due September 1, 2006Acceptance Notification January 1, 2007Final Manuscript Due April 1, 2007Publication Date 3rd Quarter 2007GUEST EDITORS:Chia-Wen Lin, Department of Computer Science andInformation Engineering, National Chung ChengUniversity, Chiayi 621, Taiwan; cwlin@cs.ccu.edu.twYap-Peng Tan, School of Electrical and ElectronicEngineering, Nanyang Technological University, NanyangAvenue, Singapore 639798, Singapore; eyptan@ntu.edu.sgMing-Ting Sun, Department of Electrical Engineering,University of Washington, Seattle, WA 98195, USA ;sun@ee.washington.eduAlex Kot, School of Electrical and Electronic Engineering,Nanyang Technological University, Nanyang Avenue,Singapore 639798, Singapore; eackot@ntu.edu.sg

Anthony Vetro, Mitsubishi Electric Research Laboratories,201 Broadway, 8th Floor, Cambridge, MA 02138, USA;avetro@merl.comHindawi Publishing Corporationhttp://www.hindawi.com

EURASIP JOURNAL ON APPLIED SIGNAL PROCESSINGSpecial Issue onKnowledge-Assisted Media Analysis for InteractiveMultimedia ApplicationsCall for PapersIt is broadly acknowledged that the development of enablingtechnologies for new forms of interactive multimedia servicesrequires a targeted confluence of knowledge, semantics,and low-level media processing. The convergence of these areasis key to many applications including interactive TV, networkedmedical imaging, vision-based surveillance and multimediavisualization, navigation, search, and retrieval. Thelatter is a crucial application since the exponential growthof audiovisual data, along with the critical lack of tools torecord the data in a well-structured form, is rendering uselessvast portions of available content. To overcome this problem,there is need for technology that is able to produce accuratelevels of abstraction in order to annotate and retrieve contentusing queries that are natural to humans. Such technologywill help narrow the gap between low-level features orcontent descriptors that can be computed automatically, andthe richness and subjectivity of semantics in user queries andhigh-level human interpretations of audiovisual media.This special issue focuses on truly integrative research targetingof what can be disparate disciplines including imageprocessing, knowledge engineering, information retrieval,semantic, analysis, and artificial intelligence. High-qualityand novel contributions addressing theoretical and practicalaspects are solicited. Specifically, the following topics are ofinterest:• Semantics-based multimedia analysis• Context-based multimedia mining• Intelligent exploitation of user relevance feedback• Knowledge acquisition from multimedia contents• Semantics based interaction with multimedia• Integration of multimedia processing and SemanticWeb technologies to enable automatic content sharing,processing, and interpretation• Content, user, and network aware media engineering• Multimodal techniques, high-dimensionality reduction,and low level feature fusionAuthors should follow the EURASIP JASP manuscriptformat described at http://www.hindawi.com/journals/asp/.Prospective authors should submit an electronic copy oftheir complete manuscript through the EURASIP JASP manuscripttracking system at http://www.hindawi.com/mts/ accordingto the following timetable:Manuscript Due September 1, 2006Acceptance Notification January 15, 2007Final Manuscript Due April 1, 2007Publication Date 3rd Quarter, 2007GUEST EDITORS:Ebroul Izquierdo, Department of Electronic Engineering,Queen Mary, University of London, Mile End Road, LondonE1 4NS, United Kingdom; ebroul.izquierdo@elec.qmul.ac.ukHyoung Joong Kim, Department of Control and InstrumentationEngineering, Kangwon National University, 192 1Hyoja2 Dong, Kangwon Do 200 701, Korea;khj@kangwon.ac.krThomas Sikora, Communication Systems Group, TechnicalUniversity Berlin, Einstein Ufer 17, 10587 Berlin, Germany;sikora@nue.tu-berlin.deHindawi Publishing Corporationhttp://www.hindawi.com

EURASIP JOURNAL ON APPLIED SIGNAL PROCESSINGSpecial Issue onAdvanced Signal Processing and ComputationalIntelligence Techniques for Power Line CommunicationsCall for PapersIn recent years, increased demand for fast Internet access andnew multimedia services, the development of new and feasiblesignal processing techniques associated with faster andlow-cost digital signal processors, as well as the deregulationof the telecommunications market have placed major emphasison the value of investigating hostile media, such aspowerline (PL) channels for high-rate data transmissions.Nowadays, some companies are offering powerline communications(PLC) modems with mean and peak bit-ratesaround 100 Mbps and 200 Mbps, respectively. However,advanced broadband powerline communications (BPLC)modems will surpass this performance. For accomplishing it,some special schemes or solutions for coping with the followingissues should be addressed: (i) considerable differencesbetween powerline network topologies; (ii) hostile propertiesof PL channels, such as attenuation proportional to high frequenciesand long distances, high-power impulse noise occurrences,time-varying behavior, and strong inter-symbolinterference (ISI) effects; (iv) electromagnetic compatibilitywith other well-established communication systems workingin the same spectrum, (v) climatic conditions in differentparts of the world; (vii) reliability and QoS guarantee forvideo and voice transmissions; and (vi) different demandsand needs from developed, developing, and poor countries.These issues can lead to exciting research frontiers withvery promising results if signal processing, digital communication,and computational intelligence techniques are effectivelyand efficiently combined.The goal of this special issue is to introduce signal processing,digital communication, and computational intelligencetools either individually or in combined form for advancingreliable and powerful future generations of powerline communicationsolutions that can be suited with for applicationsin developed, developing, and poor countries.Topics of interest include (but are not limited to)• Multicarrier, spread spectrum, and single carrier techniques• Channel modeling• Channel coding and equalization techniques• Multiuser detection and multiple access techniques• Synchronization techniques• Impulse noise cancellation techniques• FPGA, ASIC, and DSP implementation issues of PLCmodems• Error resilience, error concealment, and Joint sourcechanneldesign methods for video transmissionthrough PL channelsAuthors should follow the EURASIP JASP manuscript formatdescribed at the journal site http://asp.hindawi.com/.Prospective authors should submit an electronic copy of theircomplete manuscripts through the EURASIP JASP manuscripttracking system at http://www.hindawi.com/mts/, accordingto the following timetable:Manuscript Due October 1, 2006Acceptance Notification January 1, 2007Final Manuscript Due April 1, 2007Publication Date 3rd Quarter, 2007GUEST EDITORS:Moisés Vidal Ribeiro, Federal University of Juiz de Fora,Brazil; mribeiro@ieee.orgLutz Lampe, University of British Columbia, Canada;lampe@ece.ubc.caSanjit K. Mitra, University of California, Santa Barbara,USA; mitra@ece.ucsb.eduKlaus Dostert, University of Karlsruhe, Germany;klaus.dostert@etec.uni-karlsruhe.deHalid Hrasnica, Dresden University of Technology, Germanyhrasnica@ifn.et.tu-dresden.deHindawi Publishing Corporationhttp://asp.hindawi.com

EURASIP JOURNAL ON APPLIED SIGNAL PROCESSINGSpecial Issue onNumerical Linear Algebra in Signal ProcessingApplicationsCall for PapersThe cross-fertilization between numerical linear algebra anddigital signal processing has been very fruitful in the lastdecades. The interaction between them has been growing,leading to many new algorithms.Numerical linear algebra tools, such as eigenvalue and singularvalue decomposition and their higher-extension, leastsquares, total least squares, recursive least squares, regularization,orthogonality, and projections, are the kernels of powerfuland numerically robust algorithms.Thegoalofthisspecialissueistopresentnewefficient andreliable numerical linear algebra tools for signal processingapplications. Areas and topics of interest for this special issueinclude (but are not limited to):• Singular value and eigenvalue decompositions, includingapplications.• Fourier, Toeplitz, Cauchy, Vandermonde and semiseparablematrices, including special algorithms andarchitectures.• Recursive least squares in digital signal processing.• Updating and downdating techniques in linear algebraand signal processing.• Stability and sensitivity analysis of special recursiveleast-squares problems.• Numerical linear algebra in:• Biomedical signal processing applications.• Adaptive filters.• Remote sensing.• Acoustic echo cancellation.• Blind signal separation and multiuser detection.• Multidimensional harmonic retrieval and direction-of-arrivalestimation.• Applications in wireless communications.• Applications in pattern analysis and statisticalmodeling.• Sensor array processing.Authors should follow the EURASIP JASP manuscriptformat described at http://www.hindawi.com/journals/asp/.Prospective authors should submit an electronic copy oftheir complete manuscript through the EURASIP JASP manuscripttracking system at http://www.hindawi.com/mts/, accordingto the following timetable:Manuscript Due October 1, 2006Acceptance Notification February 1, 2007Final Manuscript Due May 1, 2007Publication Date 3rd Quarter, 2007GUEST EDITORS:Shivkumar Chandrasekaran, Department of Electricaland Computer Engineering, University of California, SantaBarbara, USA; shiv@ece.ucsb.eduGene H. Golub, Department of Computer Science, StanfordUniversity, USA; golub@sccm.stanford.eduNicola Mastronardi, Istituto per le Applicazioni del Calcolo“Mauro Picone,” Consiglio Nazionale delle Ricerche, Bari,Italy; n.mastronardi@ba.iac.cnr.itMarc Moonen, Department of Electrical Engineering,Katholieke Universiteit Leuven, Belgium;marc.moonen@esat.kuleuven.bePaul Van Dooren, Department of Mathematical Engineering,Catholic University of Louvain, Belgium;vdooren@csam.ucl.ac.beSabine Van Huffel, Department of Electrical Engineering,Katholieke Universiteit Leuven, Belgium;sabine.vanhuffel@esat.kuleuven.beHindawi Publishing Corporationhttp://www.hindawi.com