1934 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012

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1 1934 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012 Side-Information-Dependent Correlation Channel Estimation in Hash-Based Distributed Video Coding Nikos Deligiannis, Member, IEEE, Joeri Barbarien, Marc Jacobs, Adrian Munteanu, Member, IEEE, Athanassios Skodras, Senior Member, IEEE, and Peter Schelkens, Member, IEEE Abstract In the context of low-cost video encoding, distributed video coding (DVC) has recently emerged as a potential candidate for uplink-oriented applications. This paper builds on a concept of correlation channel (CC) modeling, which expresses the correlation noise as being statistically dependent on the side information (SI). Compared with classical side-information-independent (SII) noise modeling adopted in current DVC solutions, it is theoretically proven that side-information-dependent (SID) modeling improves the Wyner Ziv coding performance. Anchored in this finding, this paper proposes a novel algorithm for online estimation of the SID CC parameters based on already decoded information. The proposed algorithm enables bit-plane-by-bit-plane successive refinement of the channel estimation leading to progressively improved accuracy. Additionally, the proposed algorithm is included in a novel DVC architecture that employs a competitive hash-based motion estimation technique to generate high-quality SI at the decoder. Experimental results corroborate our theoretical gains and validate the accuracy of the channel estimation algorithm. The performance assessment of the proposed architecture shows remarkable and consistent coding gains over a germane group of state-of-the-art distributed and standard video codecs, even under strenuous conditions, i.e., large groups of pictures and highly irregular motion content. Index Terms Correlation channel (CC), distributed video coding (DVC), hash information, online successively refined channel estimation, overlapped block motion estimation (OBME). I. INTRODUCTION UPLINK-ORIENTED power-constrained applications, e.g., wireless multimedia sensors, require the design of novel video coding architectures, providing low-cost encoding, robustness against transmission errors, and high compression Manuscript received June 01, 2011; revised October 05, 2011 and November 14, 2011; accepted December 01, Date of publication December 22, 2011; date of current version March 21, This work was supported by the FWO Flanders under Project G and Project G The work of P. Schelkens was supported under a FWO postdoctoral fellowship grant. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Pascal Frossard. N. Deligiannis, M. Jacobs, A. Munteanu, and P. Schelkens are with the Department of Electronics and Informatics, Vrije Universiteit Brussel, B-1050 Brussels, Belgium, and also with the Interdisciplinary Institute for Broadband Technology, B-9050 Ghent, Belgium ( ndeligia@etro.vub.ac.be; mjacobs@etro.vub.ac.be; acmuntea@etro.vub.ac.be; pschelke@etro.vub.ac. be). J. Barbarien was with the Department of Electronics and Informatics, Vrije Universiteit Brussel, B-1050 Brussels, Belgium, and also with the Interdisciplinary Institute for Broadband Technology, B-9050 Ghent, Belgium. He is now with Technicolor ( jbarbarie@etro.vub.ac.be). A. Skodras is with the Department of Computer Science, Hellenic Open University, GR Patras, Greece ( skodras@eap.gr). Digital Object Identifier /TIP efficiency. Potential solutions to satisfy these requirements are rooted in distributed source coding (DSC) principles, laid in [1] and [2]. Triggered by DSC theory [1], [2], distributed video coding (DVC) became a new coding paradigm supporting the aforementioned demands in video coding. By exploiting the inherent correlation present in the video sequence at the decoder, DVC systems [3] [6] allow for a complexity shift from the encoder to the decoder, being in contrast to conventional video coding [7]. Since good DSC codes are based on channel coding [4], [8], [9], distributed joint-source channel coding of video provides robustness against transmission errors [4], [10]. In addition, layered Wyner Ziv (WZ) coding [11] enables scalable video coding [12], [13]. Furthermore, the DSC theory facilitates efficient multiview video coding without requiring intercamera communication [14], [15]. Lately, DSC principles found application in flexible video decoding [16] and flexible distribution of complexity [17]. In the context of DVC, Girod et al.introduced a feedback channel-based WZ architecture [4]. At the outset, motion-compensated interpolation (MCI) or extrapolation were employed to generate side information (SI) at the decoder [18]. An improved MCI technique was later integrated in the DISCOVER codec [5], providing state-of-the-art performance. However, MCI fails to capture fast and irregular motion mainly due to blind motion estimation [19], [20]. To overcome this problem, effective SI creation approaches are based on successive refinement or on hash-based motion estimation. In successively refined motion estimation, the decoder upgrades the quality of the SI when additional information is decoded [21]. Other schemes propose the transmission of auxiliary (hash) information to the decoder in order to augment SI creation [22] [25]. In this context, we have proposed overlapped block motion estimation and probabilistic compensation (OBMEPC) [26], which enables accurate capturing of motion using a coarse version of the original frame. Although, in DSC theory, the codec is assumed to have perfect knowledge of the correlation statistics, in a practical DVC system, the correlation channel (CC) noise can never be directly measured. Hence, accurate CC modeling is needed. Existing approaches construct an additive CC model in which the noise is assumed to be independent of the channel input signal. In early works, a zero-mean Laplacian noise model is employed, of which the scaling parameter is assumed temporally and spatially stationary [4]. Then, Westerlaken et al. [27] argued that, by differentiating the noise scaling parameter for occluded and nonoccluded areas, the overall coding performance is improved. In [28], however, they showed that segmentation inaccuracies notably reduce the convenience of the nonstationary model. In the /$ IEEE

2 DELIGIANNIS et al.: SID CCE IN HASH-BASED DVC 1935 state-of-the-art MCI-based DVC architecture, Brites and Pereira [29] proposed a spatially stationary Laplacian model and performed online estimation of its scaling parameter per WZ frame/ discrete cosine transform (DCT) band at the decoder side. Since the quality of the SI fluctuates spatially, Brites and Pereira [29] also proposed an estimation based on block and pixel/dct coefficients. However, along the lines of [28], adaptation in smaller spatial regions does not necessarily lead to improved performance since online estimation becomes imprecise due to limited statistical support information. To improve the estimation, progressive refinement of the noise variance upon decoding of each DCT band is proposed in [30] and [31]. Alternatively, Esmaili and Cosman [32] performed block-based classification of the Laplacian scaling parameter using profiles obtained by offline training. Recently, a CC estimation (CCE) technique, which exploits the inter-bit-plane correlation using particle filtering, has been proposed by Stankovic et al. [33] in a pixel-domain architecture without a feedback channel. In contrast to existing models, we have introduced an additive CC model in which the noise depends on the channel input signal. Since the SI signal is the input of the considered channel, the term side-information-dependent (SID) correlation noise model is used [34] [36]. In particular, Deligiannis et al. [34], [35] introduced the concept of SID modeling in the pixel domain and validated it experimentally using a fitting error metric (i.e., conditional relative entropy), i.e., minimized offline. Optimal SI, by means of a motion oracle, was assumed in [34], whereas in [35], SI was generated using OBMEPC [26]. The improvement in fitting accuracy, brought by SID modeling, was first shown to imply rate savings in [36]. This paper builds on the concepts introduced in our prior works [26], [34] [36] and brings four major contributions. First, this paper thoroughly quantifies the theoretical coding gain that the SID model brings over classical side-information-independent (SII) modeling. Second, conversely to [34] [36], this paper presents SID modeling in the transform domain and proposes a novel algorithm for online SID estimation. It is important to note that, contrary to other methods, i.e., [29] [32], the proposed algorithm enables bit-plane-by-bit-plane refinement of CCE providing improved accuracy. Third, the proposed online SID algorithm is included in a novel efficient hash-based DVC architecture. Compared with our previous work [26] and other solutions in the literature [22] [25], the presented architecture comprises a novel approach to form and to compress a hash per WZ frame, which imposes a negligible complexity and memory overhead at the encoder. In addition, in contrast to the system of [26], the presented architecture codes each WZ frame using a transform-domain WZ (TDWZ) coding scheme. Fourth, at the decoder, high-quality SI is produced by a new OBME and compensation with subsampled matching (OBMEC/SSM) technique. OBMEC/SSM notably advances over our OBMEPC in [26], by employing a different matching strategy and predictors that improve the prediction quality and reduce the computational complexity. Experimental results corroborate the theoretical rate distortion (RD) gains brought by SID modeling. The experiments also show that the algorithm for online SID estimation yields improved performance compared with state-of-the-art techniques [29], [31]. Moreover, the assessment of the proposed hash coding method and the new OBMEC/SSM technique shows notable gains compared with the approaches of [26]. Additionally, the proposed system is shown to bring significant compression improvements over a broad suite of state-of-the-art codecs targeting low-cost video encoding, including DISCOVER [5], H.264/AVC Intra and No Motion [7], the hash-based codec of [23], and our previous codec [26]. The rest of this paper is structured as follows. The rationale of the SID model and its theoretical gain against SII modelingaregiveninsectionii.theproposedhash-baseddvc system is discussed in Section III, whereas the hash codec and the OBMEC/SSM method are detailed in Sections IV and V, respectively. In Section VI, our novel algorithm to realize online successively refined SID correlation estimation is presented. The experimental validation of the proposed techniques is given in Section VII, whereas Section VIII concludes this paper. II. SID CC MODELING A. CC Modeling in DSC In DSC, the correlation is often expressed by an additive noise channel model,,wherethesi is the input, and the source is the output of the channel (see [37]). Hence, the relation between the conditional probability density function (pdf) of the channel output and the conditional pdf of the noise, given the input, is expressed as [38] Assuming that the correlation noise is independent of the channel input signal [4], [11], i.e., is independent of in the considered channel model [37], the CC model is simplified to We observe that most DVC modeling approaches, e.g., [4], [27] [32], assume the correlation noise as independent zero-mean Laplacian with standard deviation, i.e.,, as follows: Therefore, the pdf of the source,giventhesi,isexpressed by a Laplacian distribution centered on having standard deviation,i.e.,. The independent noise component of (3) has been considered stationary at different levels. In pixel-domain systems, the noise parameter is estimated at sequence level [4], [29], frame level [29], block level [29], or pixel level [29]. Analogously, in transform-domain systems, the noise parameter is estimated per band of the sequence [22], [29], per band of each WZ frame (band level) [29], [39], or per DCT coefficient (coefficient level) [29] [31]. Since, in the referred cases, the noise is independent of the channel input, i.e., the SI (see Fig. 1(a) for the schema of the channel), we call such modeling approaches as SII noise modeling [34] [36]. (1) (2) (3)

3 1936 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012 Fig. 1. Schema of (a) SII (symmetric) noise model [11] and (b) SID (asymmetric) noise model. The dashed line expresses the noise dependence on the SI. Contrary to the SII approach, we have proposed a different CC modeling concept [34], [35], in which the distribution of the noise depends on the channel input signal, i.e., the SI. Specifically, our SID [34], [35] channel model [see Fig. 1(b)] assumes the noise as being zero-mean Laplacian with the standard deviation, which varies depending on the realization of the SI (input of the channel), i.e.,,asfollows: Since the correlation noise is additive, (4) implies that, for every realization of the SI alphabet, the pdf of the channel output is given by a Laplacian distribution centered on, having a standard deviation, which varies with,i.e.,. The validation of our SID channel model in the pixel-domain has been given in [34], [35]. The SID model accurately captures the empirical conditional probability mass function (PMF), i.e.,, calculated based on the sample values of awzframe and its SI at a given spatial stationarity level. In effect, it has been shown in [34], [35] that, compared with the SII model, the SID model brings a vast reduction of the fitting mismatch for a large set of video sequences. The reported improvements are consistent irrespective of the quality of the SI. In particular, an optimal motion oracle and OBMEPC [26] has been employed in [34] and [35], respectively. Furthermore, the SID is more accurate than the SII model for various levels of assumed noise stationarity, including frame level [34] and block level [35]. Let the SI values stem from a discrete alphabet with elements. Then, the projections of the SII and SID CC pdfs onto the -plane are given in Fig. 2(a) and (b), respectively. For an assumed noise stationarity level, in the SII (or input independent) model, the noise variance is constant and independent of the SI [see Fig. 2(a)]. In contrast, in the SID (or input dependent) model, the noise variance depends on the realization of the SI [see Fig. 2(b)]. Following the terminology of channel symmetry in [40], the SII model can be interpreted as a -ary-input continuous-output symmetric Laplacian channel. Conversely, the SID model is equivalent to a -ary-input continuous-output asymmetric Laplacian channel. Notice also that, in case both and have a binary alphabet, the SII and SID channel models come down to the binary symmetric channel and the binary asymmetric channel models, correspondingly. In this paper, we analytically provethat,atthesamenoisestationarity level, SID channel modeling yields compression gains over classical SII modeling. Driven by this finding, we propose a novel transform-domain online SID estimation method, which considers band-level SID noise stationarity, i.e., it is applied per (4) Fig. 2. Projection of the (a) SII and (b) SID CC distribution models for an assumed noise stationarity level. coded DCT band of each WZ frame. The proposed algorithm enables bit-plane-by-bit-plane successively refined SID correlation estimation, yielding significant gains over the offline SII band-level method of [29], and the online SII coefficient-level TRACE technique [31]. B. Theoretical Analysis of SID CC Modeling We consider a WZ scheme consisting of uniform scalar quantization followed by ideal Slepian Wolf (SW) coding [41]. This scheme has been shown to deliver WZ coding performance equivalent to entropy-coded scalar quantization in nondistributed coding [41]. To simplify the calculations, known asymptotic results [41], [42] are employed when necessary. The following holds. Lemma 1: The L-2 distortion for a Laplacian source quantized using a uniform scalar quantizer centered on its mean is given by where is the cell size of the quantizer and is the standard deviation. Proof: The proof is sketched in the Appendix. Remark 1: According to high rate results for distributed quantization [41], the optimal SW-coded scalar quantizer for smooth pdfs is the uniform quantizer. Based on lemma 1, the following is derived. Lemma 2: Let, be the L-2 distortion of the SID and SII Laplacian models, respectively. A necessary condition so that the SID distortion be equal to the SII distortion for any,i.e.,,isgivenby where and are the standard deviations of the SID and SII models, respectively, and is the pdf of the SI. Proof: The proof is given in the Appendix. Remark 2: We note that the employed uniform scalar quantizer is centered on the mean of each Laplacian distribution in order to achieve the upper bound in the WZ source coding gain [41]. Based on these two lemmas, the following holds. (5) (6)

4 DELIGIANNIS et al.: SID CCE IN HASH-BASED DVC 1937 Fig. 3. Block diagram of the proposed hash-based DVC scheme. The novel modules of the codec are highlighted in gray. Theorem 1: Under high rate assumptions and considering the SID and SII models distortions equal where and are rates for a distortion level, as given by an SID and SII Laplacian models, respectively, and is the expectation operator. Proof: The proof is given in the Appendix. In words, Theorem 1 specifies that, for a given L-2 distortion, an SID channel exhibits higher or equal CC capacity compared with an SII channel. Hence, for a given L-2 distortion, SW random binning for an SID channel is more efficient compared with that for an SII channel. This intrinsic coding gain is verified in Section VII-A. III. PROPOSED HASH-BASED DVC ARCHITECTURE The proposed hash-based DVC architecture is illustrated in Fig. 3. At the encoder, the input video sequence is organized into groups of pictures (GOPs) and is decomposed into key frames, i.e., the first frame in each GOP, and WZ frames. The key frames, denoted by, are encoded using H.264/AVC Intra frame coding, adhering to the main profile, as configured in [5]. For each WZ frame, a novel hash is sent to aid SI creation at the decoder, as detailed in Section IV. In addition to the coded hash, a WZ bit stream is formed for each WZ frame, based on the TDWZ architecture [4]. Conversely, to other hash-driven DVC schemes, e.g., [23], the WZ encoder is chosen to encode the original WZ frame, i.e., rather than its difference with the hash, in order to preserve the error-resilient traits of WZ coding for the entire WZ frames waveform [4], [8]. We also note that the proposed TDWZ system is built on the principles of layered WZ coding [11]. These principles have found application in alternative scalable WZ video coding schemes, e.g., in [12] and [13]. Contrary to these works in which the computational expensive tasks for motion estimation, rate control, and mode decision are performed at the encoder, the proposed scheme aims at efficient yet low-cost DVC. (7) Specifically, at the encoder, the WZ frame s pixel values are transformed using the 4 4 separable integer transform, as in H.264/AVC [7], which has properties similar to the DCT. Using a set of predefined quantization matrices (QMs), each DCT band is independently quantized with levels. A uniform and a double-deadzone scalar quantizer are employed for the DC and AC bands, respectively. After quantization, the quantization indices are converted into binary codewords and fed to the SW encoder. At this point, we notice that the employed SW coding needs to cope with the asymmetric nature of the SID CC (see Section II-A). In [43], McEliece argued that good communication codes with Turbo-like decoders could be effectively used on asymmetric channels. Furthermore, in [44], Wang et al. extended density evolution, which is a strong analytical tool of low-density parity-check (LDPC) codes, to asymmetric channels and proposed good code constructions. Thus, SW coding is realized using the rate-adaptive LDPC accumulate (LDPCA) codes of [9], of which the performance is not degraded even when the CC features strong asymmetries, e.g., Z-channel statistics [9]. The derived syndrome bits per codeword are stored in a buffer, and a feedback channel is used to allow optimal rate control [4]. At the decoder, the key frames are H.264/AVC Intra decoded and stored in a reference frame buffer. The hash information is decoded by inverting the tasks applied at the encoder. Next, the new OBMEC/SSM technique is used to generate a motion-compensated prediction of the WZ frame based on the received hash and reference frames, as detailed in Section V. Subsequently, the produced motion-compensated frame is DCT transformed, forming the SI for the WZ codec. Thereafter, per coded DCT band of the WZ frame, the decoder performs the proposed novel online SID CCE algorithm, which is described in Section VI-B. Per band of the WZ frame, the decoder produces soft estimates to decode the WZ bit-planes based on the SID channel estimates and the value of each SI coefficient. After decoding each bit-plane of a band of the WZ frame, the bit-plane is stored in a buffer, and the algorithm is executed again, enabling bit-plane-by-bit-plane progressive refinement of the estimates. After decoding the final WZ

5 1938 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012 the proposed hash encoder, thereby preventing error propagation between the hash data of consecutive WZ frames. Fig. 4. Hash formation and spatial prediction processes. On the left, gray circles with solid lines denote the subsampled original pixel values. On the right, dashed circles signify the MSB of the subsampled pixel values. bit-plane of the band of the WZ frame, the estimates are again updated, yielding improved estimation for the reconstruction process. After decoding all the bit-planes of all WZ coded bands of the WZ frame, MMSE reconstruction [8] and an inverse DCT are carried out, yielding the reconstructed WZ frame. IV. HASH INFORMATION CODING The proposed hash information consists of the most significant bit-plane (MSB) of the dyadically subsampled luma component of the original WZ frame. To encode this information, each binary value at position in the hash is first spatially predicted. The employed prediction scheme is essentially a low-complexity binary equivalent of the well-known edge-adaptive JPEG-LS predictor [45]. More specifically, each binary value is predicted by a Boolean function,with,,and,denoting the left, top, and top-left neighboring binary values in the hash, respectively, as shown in Fig. 4. After the prediction stage, each prediction error is directly calculated using a single exclusive OR operation between the predictor and the predicted value. Finally, each binary symbol is coded using multiplication-free context-based binary arithmetic coding employing one of eight different probability models. The probability model is selected based on the neighboring local gradients,,and,intheoriginal hash,with denoting the top-right neighbor of the predicted value (see Fig. 4). Notice that the proposed hash formation and coding processes are designed in order to impose a limited complexity and memory usage overhead at the encoder. First, conversely to our prior work [26], the hash is formed based on the subsampled pixel values, requiring only 1/4 of the samples to be further processed. Second, the spatial prediction process can be implemented using simple binary arithmetic, making it ideal for hardware implementation. Third, in contrast to alternative solutions, e.g., [22] [25], the proposed technique does not perform any block-based decisions on the transmission of hash information at the encoder side. Hence, it is neither burdened by the computationally expensive block-based comparisons required for such mode decision nor does it require storing reference information from temporally adjacent frames. Lastly, contrary to [25] and [26], no temporal prediction is applied by V. HASH-BASED MOTION ESTIMATION AT THE DECODER In this section, we describe OBMEC/SSM, a technique which generates accurate SI at the decoder based on the proposed hash. Compared with our previous OBMEPC [26] technique, OBMEC/SSM not only operates on a different hash but also incorporates novel tools to further improve the prediction quality and to reduce the computational complexity. A. Bit-Plane OBME With Subsampled Reference Frames OBME is performed in a hierarchical bidirectional prediction structure similar to MCI-based DVC systems [4], [5]. Using two previously decoded WZ and/or key frames as past and future reference frames, the decoder performs OBME based on the proposed hash. Specifically, let denote the reference frames and let denote the MSB of the luma component of. In addition, let denote the decoded hash frame. Due to the lower resolution of the hash frame,thevaluesin each binary frame are reorganized into four subsampled reference frames, i.e.,,, by separating the values at even and odd positions in as (see Fig. 5). In this way, the newly formed binary reference frames have the same resolution as the hash frame, hence facilitating the execution of OBME. Next, based on and, downscaled motion vectors between the WZ frame and the reference frames are found by OBME. Note that OBME derives more than one motion vector per pixel, thereby decreasing the energy of the prediction error compared with other methods, e.g., [5]. In addition, blocking artifacts are drastically reduced, thus increasing the subjective quality of the decoded frame. The OBME process proceeds as follows. Using an overlap step size, the hash frame is divided into overlapping blocks of size samples, with top-left coordinates. For each overlapping block, the best matching block within a specified search range is found in one of the subsampled reference frames. The employed matching criterion maximizes the number of binary values in the hash block that are identical to the colocated binary values in block, where is the associated motion vector. Therefore, for each overlapping block in the hash frame, OBME has identified the motion vector and indices,,and,which define the best reference block. In addition, OBME retains the matching strength for the block,whichwillbe used in the SI generation process. The matching strength is defined as the number of binary values in that is identical to the colocated binary values in divided by,i.e.,the total number of samples in a block. We remark that due to the nature of the new hash, the matching process is carried out with binary comparisons, thereby vastly diminishing the associated complexity. B. SI Generation To generate SI, the motion vectors derived by OBME are first upsampled. By construction, there is a direct correspondence

6 DELIGIANNIS et al.: SID CCE IN HASH-BASED DVC 1939 Fig. 5. Creation of the subsampled reference frames in the OBME process. between every block (with top-left coordinates and size pixels) in the hash frame and the block (with top-left coordinates and size pixels) covering the SI frame.asaresult,basedon and,,and found by OBME for the hash block, an equivalent motion vector, i.e.,, to the original reference frame,is derived for the SI block. In this way, for each overlapping block in the SI frame, a temporal predictor block, which is denoted by, is found in the reference frame. After scaling the motion vectors, SI is generated by multihypothesis pixel-based compensation. Specifically, each pixel in the SI frame belongs to a number of overlapping blocks. This means that, each pixel in the SI frame is linked to a number of predictors,being the colocated pixel values in the blocks. Each SI pixel value is then derived by properly combining its predictors. In detail, for the even pixel positions in the SI frame, the MSB of the original frame was transmitted in the hash.this binary value is used to determine the weight of the predictor during compensation. Specifically, if the binary value in the hash agrees with the MSB of a predictor, then the predictor is said to be verified, and its weight is equal to the associated matching strength. Otherwise, the predictor is categorized as unverified, and its weight is empirically set to the lowest value, i.e.,. For the other pixels in the SI frame, for which hash information is unavailable, simple averaging of their corresponding predictors is applied to derive the SI values. We observe that, in [26], after motion estimation, there could be pixels without having any predictor. These pixels required an extra reconstruction phase. However, in OBMEC/SSM, all pixels are assigned with a set of predictors, as explained above, thereby yielding superior pixel value reconstruction. Lastly, we note that OBMEC/SSM also uses the motion vectors generated by OBME to produce the chroma components of the WZ frame at the decoder, generating candidate predictors based on the chroma components of the reference frames. The weights derived for the even positions in the luma component are employed in the weighted averaging of the predictors. VI. SID CCE In this section, we present our novel algorithm for online SID CCE.First,wepresentanoffline SID channel estimator that serves as a reference enabling the accuracy assessment of our online algorithm. A. Offline SID Correlation Estimation Offline SID correlation estimation is an ideal but unrealistic approach since it assumes that the original frame pixel values are present at the decoder. Under this assumption, the decoder can form the transformed noise frame, i.e.,,,where and denote the DCTtransformed WZ and SI frame s coefficient of the band and block. Per coded band of a WZ frame, offline SID estimates are independently determined. We note that DCT coefficients are real numbered; therefore, practically, in order to have a discrete number of SID sigma values, one needs to group the SI frame coefficients of each band. 1 Then, for every coded band and transformed SI quantization index, the corresponding offline SID estimate is (8) where denotes the quantization of the SI frame coefficients of band and is the number of quantization levels (QLs) for band. Equation (8) implies that, per band of a WZ frame, is estimated as the standard deviation of the transformed noise frame coefficients of which the corresponding SI coefficient value falls into the bin indexed by. For both the proposed offline and online SID algorithms, the best RD performance is obtained based on a balance between the SID noise stationarity level, the number of QLs of the SI frame coefficients per band, and the statistical support to accurately estimate the SID parameters. Concerning stationarity, SID estimation can be applied to any small number of spatially neighboring DCT coefficients,whichbelong to the same DCT band, i.e.,. However, note that, although using small spatial stationarity levels enables better adjustment to fluctuating spatial statistics, precision can be challenged by inaccurate estimation of the parameters due to narrow statistical support. Additionally, the number of QLs used in the quantization of the SI coefficients affects the accuracy of channel estimation. A low number of QLs drives the system to the SII paradigm. On the other hand, a high number of QLs reduces the statistical support, hence deteriorating the RD performance. We have empirically observed that the highest RD 1 Notice that grouping (quantization) of the SI coefficients is only performed for discretizing their alphabet in order to enable SID CCE. When deriving the soft-decoding log-likelihood ratios and during MMSE reconstruction, the actual values of the SI coefficients are used.

7 1940 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012 performance for the proposed offline and online SID algorithms is obtained when the SID channel is considered stationary at band level, and the number of QLs per band is identical to the number of QLs employed to quantize the values of the transformed original frame at the encoder. B. Online Successively Refined SID Correlation Estimation The designed online SID algorithm notably advances over contemporary online correlation estimation methods. First, apart from few exceptions, e.g., [31], most online methods [29], [30], [32], are solely created for MCI-based systems. On the contrary, the proposed technique is intended to not limit its applicability to a specific DVC architecture. Moreover, the proposed technique is the first to capture the dependence of the correlation noise on the SI, thereby exploiting the inherent coding gain of SID (asymmetric) channel modeling. What is more, unlike alternative methods [30], [31], which refine the SII model parameters across DCT bands of a WZ frame, the proposed technique improves the estimation accuracy by enabling a finer refinement of the SID model parameters, i.e., across the bit-planes of a band of a WZ frame. As explained above, the proposed online algorithm guarantees adequate statistical support for accurate SID estimation by balancing the SID stationarity level and the number of QLs of the SI coefficients per band. For any coded band of a WZ frame, the successive refinement algorithm is initiated after SW decoding (SWD) of the first bit-plane of the band. To facilitate the applicability of the algorithm to any DVC scheme, similar to [39], the estimates, which are required to decode the first bit-plane of band, are obtained using (8) in which is approximated by the transformed noise of the reconstructed previous WZ frame and its corresponding SI. 2 Since the correlation between the first WZ bit-plane and the SI is high, or else, since WZ quantization is very coarse, the estimation error caused by this approach does not notably influence the RD performance, as shown in Section VII-A. In a nutshell, the principle of our online bit-plane-by-bitplane successive refinement SID algorithm is as follows. Per coded band of a WZ frame, the algorithm combines the already SW-decoded bit-planes of the band to derive quantization indices of the WZ frame coefficients of the band. In this way, apart from the SI coefficients, the algorithm is aware of a coarse description of the WZ frame s coefficients of the band. Then, based on the available WZ quantization indices and the discretized SI coefficients of the band, i.e.,, the algorithm empirically estimates the CC conditional PMF for any given. Based on the obtained PMF for any,the algorithm derives the corresponding conditional pdf. Notice that, according to the SID model, the conditional pdf for a given is Laplacian with parameter, which depends on. The derived SID estimates are used to decode the next bit-plane. Thereafter, the decoder has access to a finer description of the band s WZ coefficients, and the algorithm is repeated. After SWD all the bit-planes of the band,the algorithm is executed again so as to refine the estimates for reconstruction. 2 Only for the very first WZ frame, one (SII) sigma per coded WZ band is estimated at the encoder similarly to [46] and sent to the decoder. This is done in order to keep the required rate to code the sigmas as low as possible. For any coded band of a WZ frame, the proposed progressive refinement algorithm is detailed in the next steps. Step 1) Let,, denote the number of decoded bit-planes of the WZ coefficients of band. These bit-planes are denoted by the binary -tuples,where, is the size of the WZ frame band. The decoder combines the available binary -tuples to produce a coarse description of the WZ coefficients of band, which are denoted by. The latter contains quantization indices of the WZ coefficients of the band in the range. 3 In addition, for the purpose of channel estimation, the decoder discretizes the SI coefficients of band (see Section VI-A), thereby producing the -tuple, containing the indices. Step 2) Using the available and -tuples, the correlation estimator approximates the joint PMF,where denotes the random variable of the quantization indices of the WZ coefficients, denotes the quantization index, or bin,,and.this approximation is performed using the histogram. Step 3) From the estimated joint PMF, the algorithm calculates the empirical conditional PMF, i.e., For every index and, (9) gives the transition matrix of a discrete CC having the discretized SI coefficients as input and the quantized WZ coefficients as output. Step 4) Each row of this transition matrix, i.e.,, is a conditional PMF for a given. The algorithm then attempts to derive the conditional pdf for a given, which would yield this empirical PMF. According to the SID paradigm, is a PMF that would be derived by scalar quantization of a Laplacian distribution that has a scaling parameter, and it is centered on, i.e., the inversely quantized SI value derived from the quantization index. Therefore, to get the scaling parameter of each Laplacian distribution, one needs to find the root of the following function: (9) (10) 3 In particular, given the available bit-planes for a given WZ coefficient, the quantizer cell indexed by is formed by merging the original, encoder-side quantizer cells for which the binary representation is prefixed by.

8 DELIGIANNIS et al.: SID CCE IN HASH-BASED DVC 1941 where, and are the lower and upper bounds of the quantization bin with index,and has replaced for simplicity. The derivation and the proof of the uniqueness of the root of (10) for every are detailed in the Appendix. After SWD of a bit-plane of the band of the WZ frame, steps 1 4 are executed again. The process is executed recursively, hence delivering a more accurate estimate with every additional decoded bit-plane of a band of the frame. This is of paramount importance since, for every additional bit-plane, WZ quantization becomes finer; thus, the impact of inaccurate channel estimation on the coding efficiency increases. VII. EXPERIMENTAL RESULTS This section evaluates the proposed SID estimation method and the compression performance of the proposed DVC system. Tests were carried out for all frames of the Foreman, Soccer, Carphone, and Silent sequences; at QCIF resolution; at a frame rate of 15 Hz; and at a GOP sizes of 2, 4, and 8. These sequences exhibit a variety of object and camera motion characteristics. In the OBMEC/SSM module, an overlap step size of, a search range of pixels, and a block size are employed. To assess the coding performance in terms of the Bjøntegaard (BD) metric [47], four RD points have been drawn corresponding to QMs 1, 5, 7, and 8 of [5]. The quantization parameters (QPs) of the H.264/AVC Intra encoder are selected in order to maintain a constant decoding quality. Although chroma (YUV) encoding is supported, results are presented only for the luma (Y) component to allow a meaningful comparison with prior art [5], [23]. A. SID Model Validation To demonstrate the precision of SID versus SII modeling, we compare the ideal SW rate, i.e.,, calculated using the offline band-level SID (see Section VI-A), the offline band-level SII [29], and the proposed online band-level SID correlation estimation algorithm (see Section VI-B). The numerical results, which are obtained with the presented hash-based DVC codec, are computed for the DC band and averaged over all WZ frames. Seven rate points are depicted, corresponding to QM 8 of [5]. The results in Fig. 6 show that, at the same stationarity level (i.e., band level), input-dependent (SID) channel modeling offers a significant reduction of the ideal SW rate compared with input-independent (SII) [29] estimation. Notice that, at high rates, the theoretical gain of SID over SII modeling, which is given by (7), agrees with the experimental measurements, thereby confirming our theory. Regarding the accuracy of the proposed online SID algorithm, one observes that, due to its successively refined nature, it performs closely to offline SID estimation. Fig. 6 also shows that the SW rate for decoding the MSB using online SID estimation is typically higher than that of using offline SII [29], but this rate loss is minor, as explained in Section VI-B. Nonetheless, the higher the SW rate, the higher the gain brought by online SID versus offline SII [29] estimation. Fig. 6. Ideal SW rate per number of coded bit-planes of the DC band for (a) Foreman GOP2 and (b) Soccer GOP8. The theoretical gain at high rates, as derived by (7), is (a) 0.38 bits/sample and (b) 0.61 bits/sample. Fig. 7 depicts the RD comparison of the proposed online band-level SID algorithm against the offline band-level SII channel estimation of [29], and the state-of-the-art coefficient-level SII TRACE method of [31]. Fig. 7 also includes the coding results obtained with offline band-level SID estimation, which serves as an ideal SID channel estimate. The results reveal that offline SID estimation yields the best coding performance among the assessed methods, thus verifying the capacity of SID versus SII modeling, as theoretically anticipated. We also notice that the RD performance achieved with online SID isclosetothatobtainedwithoffline SID estimation, hence confirming that the proposed online SID algorithm accurately estimates the SID channel statistics. It is also notable that online band-level SID estimation consistently outperforms the offline band-level SII estimation method [29], yet the latter is impractical. One observes that the coding gain brought by the proposed online SID method compared with the offline band-level SII method [29] increases with rate since progressive refinement allows the proposed online SID method to improve its estimation accuracy as more information is decoded. This trend is also shown in Fig. 6. In terms of the average BD rate metric, the proposed online band-level SID algorithm outperforms offline band-level SII estimation by 2.88%, 4.4%, 4.05%, and 3.74% in Foreman GOP2, Soccer GOP2, Carphone GOP4, and Silent GOP8, respectively. We point out that the proposed online SID algorithm delivers higher compression efficiency compared with the state-of-the-art TRACE technique [31], which models the correlation noise as input-independent (SII) and nonstationary (coefficient level). Unlike the online coefficient-level method of [29], which is solely designed for MCI-based schemes, TRACE can be employed in the proposed system. In addition, TRACE

9 1942 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012 Fig. 7. RD performance comparison of correlation estimation methods for (a) Foreman, GOP2; (b) Soccer, GOP2; (c) Carphone, GOP4; and (d) Silent, GOP8. TABLE I BD GAINS OF THE ONLINE SID ALGORITHM OVER TRACE [31] enables progressively refined estimation across decoded DCT bands, outperforming the method of [29]. Nevertheless, compared with the proposed SID estimation method, TRACE builds on the SII paradigm and supports a coarser refinement, i.e., band-based versus bit-plane-based in the proposed SID method. The BD gains brought by the proposed online SID algorithm over TRACE are summarized in Table I. We observe that the RD gains increase with the length of the GOP since the number of coded WZ frames grows. Moreover, the gains increase, i.e., up to 5.17% BD rate reduction for Soccer GOP8, when irregular video content is coded. This is anticipated since, as the quality of the SI deteriorates, the impact of accurate correlation estimation on the RD performance increases. B. Compression Performance Figs. 8 and 9 depict the RD performance of the proposed hash-based DVC codec, equipped with online SID estimation, against a relevant set of state-of-the-art low-cost video encoding schemes. Initially, the proposed codec is compared against our previous spatial-domain unidirectional DVC (SDUDVC) scheme [26]. SDUDVC encodes the key frames and the MSBs of intermediate frames and performs OBMEPC at the decoder, using the key frames and the previous decoded frame as references. The results, depicted in Fig. 8, reveal that the proposed codec substantially advances over SDUDVC [26], yielding gains of up to 43.33% and 45.53% in BD rate reduction for Foreman and Soccer GOP8, respectively. These gains are attributed to the novel contributions of this paper. First, the employment of a DCT-domain WZ codec equipped with the proposed successive refinement online SID channel estimation method improves the RD performance and the codec s ability to achieve a wide range of rates. Second, the OBMEC/SSM technique, which is presented in Section V, significantly improves over the OBMEPC approach of [26]. Experimental results (see Fig. 10) show that, although using only a quarter of the hash information needed by OBMEPC [26], OBMEC/SSM delivers on average 0.4 and 0.8 db higher SI quality in Carphone and Soccer GOP2, respectively. Third, the proposed hash codec (see Section IV) significantly reduces the required hash coding rate over our previous approach, without using temporal prediction and being less complex.in particular, in Fig. 10, the hash rate for OBMEC/SSM using the proposed codec is 12.3 and 10.6 kb/s for Carphone and Soccer, respectively. Conversely, the hash rate for OBMEPC using the

10 DELIGIANNIS et al.: SID CCE IN HASH-BASED DVC 1943 Fig. 8. Compression performance evaluation of the proposed hash-based DVC codec for Foreman QCIF, 15-Hz (left) and Soccer QCIF, 15-Hz (right) sequences; (a) and (b) GOP2, (c) and (d) GOP4, (e) and (f) GOP8. codec of [26] is significantly higher, i.e., 48.4 and 47.6 kb/s, for Carphone and Soccer, respectively. Furthermore, the proposed codec is assessed against the state-of-the-art DISCOVER codec [5]. The RD performance of DISCOVER [5] and its execution times (see Section VII-C) have been obtained using the codec s executable, as found on the DISCOVER website [5]. The results, illustrated in Figs. 8 and 9, show that the proposed codec generally outperforms DISCOVER. Only in Silent at low rates DISCOVER surpasses the proposed codec since, for low motion content, MCI can deliver a good prediction. OBMEC/SSM requires additional hash rate, which is considerable at low rates in Silent due to low spatial correlation, thereby explaining this performance difference. Overall, in sequences with well-behaved motion, the proposed codec yields compression improvements of up to 16.59% and 2.48% BD rate reduction in Carphone and Silent GOP8, correspondingly. However, when irregular motion content is coded, the gains brought by the proposed codec against DISCOVER increase up to 31.41% and 30.95% BD rate reduction in Foreman and Soccer GOP8, respectively. These significant gains highlight the capacity of OBMEC/SSM in capturing difficult motion even in large GOPs; conditions under which MCI falls short in providing accurate prediction [19], [20].

11 1944 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012 Fig. 9. Compression performance evaluation of the proposed hash-based DVC codec for Carphone QCIF, 15-Hz (left) and Silent QCIF, 15-Hz (right) sequences; (a) and (b) GOP2, (c) and (d) GOP4, (e) and (f) GOP8. To assess the performance of the proposed scheme against that of the latest hash-based DVC prior art, the coding results of [23] are illustrated in Fig. 8. The latter creates SI by combining MCI with hash-based motion estimation using low-quality intracoded WZ frame blocks. The proposed codec improves over the system of [23] by 24.93% and 10.65% BD rate reduction in Foreman and Soccer, GOP8, respectively. These gains indicate the superior capacity of OBMEC/SSM compared with the approach of [23]. In Figs. 8 and 9, we also include the performance of H.264/AVC Intra and H.264/AVC No Motion. Note that the assessed H.264/AVC codecs exhibit higher encoding complexity than the proposed DVC solution since they feature loop filtering, intra prediction, and mode decision. The results show TABLE II PERCENTAGE CONTRIBUTION IN THE TOTAL BIT RATE AND THE RSD OF THE DECODED SEQUENCE FOR THE PROPOSED SYSTEM that, similarly to DISCOVER, the proposed scheme outperforms H.264/AVC Intra for low motion sequences at all GOPs,

12 DELIGIANNIS et al.: SID CCE IN HASH-BASED DVC 1945 TABLE III COMPARISON OF WZ DECODING EXECUTION TIMES (IN SECOND/WZ FRAME),where is the standard deviation and is the mean of the PSNR values. The very low RSD values show that the proposed codec yields a quasi-constant decoding quality. Bearing in mind that the number of LDPCAcoded bit-planes per RD point is the same with prior art [5], one concludes that the proposed novel techniques significantly increase the efficiency of WZ coding. C. Complexity Evaluation Fig. 10. SI quality for (a) Carphone, and (b) Soccer at GOP2. The dyadically subsampled MSB is used by OBMEC/SSM, while the entire MSB is used by OBMEPC. The key frames quality is identical for both methods QP. e.g., gains of up to 31.89% BD rate reduction in Silent GOP 8 are achieved. However, contrary to the other DVC codecs, the proposed scheme manages to partially outperform H.264/AVC Intra in Foreman and Carphone at all the evaluated GOP sizes, andh.264/avcnomotioninforemangop4andgop8.in Soccer, a sequence comprising very irregular motion content, the proposed codec significantly diminishes the performance gap of DVC with respect to H.264/AVC Intra and No Motion. Table II reports the percentages of the H.264/AVC Intra, the hash, and the LDPCA rate in the total rate of the proposed codec. Results are synopsized for the two extreme RD points of Foreman and Carphone, with GOP of 2 and 8. We observe that the intra and the WZ (i.e., hash and LDPCA) rate percentages agree with the portion of key and WZ frames in a GOP. Note that, for a given sequence and GOP size, the hash rate is unvarying; hence, its percentage decreases with increasing rate. The proposed codec efficiently compresses thehash,diminishing the required hash rate overhead. Recall that the hash is coded in order to enable the creation of accurate SI at the decoder and thus to increase the WZ coding performance. Table II also reports the relative standard deviation (RSD)ofthedecoded frames PSNR (both key and WZ), given by RSD In order to evaluate the computational complexity of the proposed codec, we conducted execution time tests under controlled conditions (using Pentium D CPU at 3.2 GHz, 2048 MB of RAM, and Windows XP operating system), as in [5] and [31]. At the encoder, the proposed system features the same Intra and WZ codecs as DISCOVER [5]; thus, the encoding complexity is dominated by the coding of the key frames. Note that, conversely to DISCOVER, the proposed encoder allocates additional resources to code the hash, yet this imposes very low computational and memory demands (see Section IV). In particular, results show that the hash encoder causes a negligible overhead of 1.15% and 1.19% on the encoding execution time for Foreman and Soccer, with GOP8 and QM8, respectively. Table III assesses the decoding execution time of the proposed codec with respect to DISCOVER s executable [5]. The results, which are averaged over the four RD points, show that most of the total execution time (90.79% on average) is consumed by SWD, which performs repeated LDPCA decoding using the feedback channel. OBMEC/SSM, is the second most demanding operation taking on average 9.17% of the total decoding time. For the same number of predictors per pixel and search space per predictor, OBMEC/SSM is less complex than OBMEPC [26] as it requires only 1/4 of operations for each block comparison. Concerning the complexity of CCE, we notice that the computational demands of offline SID estimation are similar to those of offline band-level SII estimation. Comparing the offline with online SID estimation, we observe that the latter is more complex due to its bit-plane-by-bit-plane successively refined behavior. Yet, the proposed online SID estimator comprises simple histogram measuring and solving nonlinear equations, which are performed efficiently using elegant algorithms. Moreover, contrary to the demanding operations of SI creation and LDPCA decoding, the online SID algorithm has a minor contribution to the total complexity (0.04% on average). In addition, Table III shows that, although online SID estimation is more complex than offline band-level SII estimation, the

13 1946 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012 overall decoding complexity is reduced since correlation estimation is more accurate. As a result, SWD requires fewer feedback channel requests and decoding operations. We point out that, although not optimized for speed, the total decoding time of the proposed codec, i.e., with online SID estimation, is vastly lower than DISCOVER s. This notable benefit is credited to the novel techniques encompassed by the proposed system, which significantly increase the decoding performance, thus reducing soft channel decoding operations. By changing the variables, the firstandthelastsumsare equal. Therefore (13) In the second term, replacing again with leads to VIII. CONCLUSION In this paper, we have investigated the concept of SID modeling of the CC in DVC. In particular, we have theoretically shown that SID modeling leads to WZ RD gains compared with the conventional SII assumption. Motivated by this finding, we have proposed a novel online SID correlation estimation algorithm. Unlike alternative algorithms, the proposed technique enables bit-plane-by-bit-plane progressive refinement of the SID model parameters, providing better accuracy. Additionally, we have presented a novel hash-based DVC architecture featuring efficient coding of low-complex hash information. This information is exploited by OBMEC/SSM, i.e., a technique that enables accurate motion estimation at the decoder. Methodical experimentation verifies the theoretical gains of SID compared with SII modeling and validates the RD improvements of the proposed online SID algorithm versus state-of-the-art correlation estimation methods [29], [31]. Furthermore, the proposed system is assessed against several relevant state-of-the-art distributed and standard video codecs. Notable compression gains are reported, e.g., up to 31.41% over DISCOVER [5], at a vastly reduced overall complexity (14) The last summation term is a geometric progression converging to. The other two integrals can be easily computed by parts, leading to (5), and ending the proof. Proof of Lemma 2: For a given, the corresponding is Laplacian with mean. The distortions of the SID and SII models are of the form given by (5), i.e., where,, whereas is a constant not depending on. The condition for any is equivalent to APPENDIX ProofofLemma1: Let be a generic Laplacian distribution and assume that the random variable is quantized using a uniform scalar quantizer centered on the mean of the distribution. The distortion resulting from quantizing is given by Let be equivalently written as (15), then (15) can (11) (16) Replacing with and expressing the above as a sum of three terms leads to One can easily verify that. Therefore, a necessary condition to have the distortions of the SID and SII models equal for any is (17) (12) Replacing and in (17) leads to (6), which ends the proof.

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Bjntegaard, Calculation of average PSNR differences between RD-curves, in ITU-Telecommunications Standardization Sector VCEG, Austin, TX, April [48] G.E.Forsythe,M.A.Malcolm,andC.B.Moler, Computer Methods for Mathematical Computations. Englewood Cliffs, NJ: Prentice-Hall, Nikos Deligiannis (S 08 M 10) received the M.Eng. degree in electrical and computer engineering from the University of Patras, Patras, Greece, in He is currently working toward the Ph.D. degree with Vrije Universiteit Brussel (VUB), Brussels, Belgium. From December 2006 to September 2007, he was with the Wireless Telecommunications Laboratory, University of Patras. He joined the Department of Electronics and Infomatics, VUB, in October His current research interests include statistical channel modeling, multimedia coding, distributed source coding, multiple description coding, wireless cellular networks, and location positioning. Mr. Deligiannis was the corecipient of the 2011 ACM/IEEE International Conference on Distributed Smart Cameras Best Paper Award. Joeri Barbarien received the M.S. degree in electrical engineering and the Ph.D. degree in engineering sciences from the Vrije Universiteit Brussel (VUB), Brussels, Belgium, in 2000 and 2006, respectively. From 2000 to 2011, he was a member of the Department of Electronics and Informatics, VUB, with the last three years as a Postdoctoral Researcher and a part-time Professor. From 2006 to 2011, he was also actively involved as a Senior Researcher and a Project Coordinator with the Interdisciplinary Institute for Broadband Technology (IBBT), Ghent, Belgium. In October 2011, he left the university and the IBBT to join Technicolor. He is the author or

16 DELIGIANNIS et al.: SID CCE IN HASH-BASED DVC 1949 coauthor of more than 50 journal and conference publications, book chapters, patent applications, and contributions to standards. His research interests include scalable video and still-image coding, distributed source coding, watermarking, implementation aspects of multimedia processing algorithms and network technology. Marc Jacobs received the M.Sc. degree in social and military sciences from the Royal Military Academy of Brussels, Brussels, Belgium, in 1992 and the M.Sc. degree in applied informatics from Vrije Universiteit Brussel (VUB), Brussels, in He started his career as an Officer at Belgian Defense, where he performed different functions within the IT-organization. Since November 2006, he has been a member with the Department of Electronics and Informatics, VUB, and the Interdisciplinary Institute for Broadband Technology, Ghent, Belgium, where he is currently working as a Researcher on video-compression-related topics. Adrian Munteanu (M 07) received the M.Sc. degree in electronics from Politehnica University of Bucharest, Bucharest, Romania, in 1994; the M.Sc. degree in biomedical engineering from University of Patras, Patras, Greece, in 1996; and the Ph.D. degree in applied sciences (awarded with the highest distinction and congratulations of the jury members) from Vrije Universiteit Brussel (VUB), Brussel, Belgium, in From 2004 to 2010, he was a postdoctoral fellow with the Fund for Scientific Research, Flanders, Belgium. Since 2007, he has been a Professor with VUB. He is also a Research Leader of the 4Media group with the Interdisciplinary Institute of Broadband Technology, Ghent, Belgium. He is the author or coauthor of more than 200 journal and conference publications, book chapters, patent applications and contributions to standards. His research interests include scalable image and video coding, distributed video coding, scalable coding of 3-D graphics, 3-D video coding, error-resilient coding, multiresolution image and video analysis, video segmentation and indexing, multimedia transmission over networks, and statistical modeling. Dr. Munteanu was the recipient of the 2004 BARCO-FWO prize for his Ph.D. work. Athanassios Skodras (M 88 SM 99) received the B.Sc. degree in physics from Aristotle University of Thessaloniki, Thessaloniki, Greece, in 1980 and the M.Eng. degree in computer engineering and informatics and the Ph.D. degree in electronics from the University of Patras, Patras, Greece, in Since 1986, he has been holding teaching and research positions with the University of Patras and CTI, Greece. Since 2002, he has been the Professor of Digital Systems and Head of Computer Science, Hellenic Open University, Patras. During the academic years 1988 to 1989 and 1996 to 1997, he has been visiting the DEEE, Imperial College, London, U.K. He is the author or coauthor of 130 papers in journals and conference proceedings, six books, three book chapters, and two international patents. His research interests include image and video coding and analysis, digital watermarking, fast transform algorithms, and real-time digital signal processing. Dr. Skodras serves as an Associate Editor for the IEEE Signal Processing Letters, thespringer Journal of Real-Time Image Processing, andtheelsevier Pattern Recognition; a Reviewer for numerous journals and conferences; and a member of many technical program committees of IEEE and other international conferences. He also serves as the Chair of the IEEE Greece Section and the Chair of the IAPR Greek Association of Image Processing and Digital Media. He was the corecipient of the First Place Chester Sall Award for the best paper in the 2000 IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, the 2005 IEEE Circuits and Systems Chapter-of-the-Year Award, and the 2009 IEEE Region 8 Circuits and Systems Chapter-of-the-Year Award. He is a Chartered Engineer and a member of the IET, EURASIP, and the Technical Chamber of Greece. Peter Schelkens (M 97) received the degree in electronic engineering in very large scale integration design from the Industriële Hogeschool Antwerpen-Mechelen (IHAM), Campus Mechelen and the M.Sc. Electrical Engineering degree in applied physics, the Biomedical Engineering degree in medical physics, and the Ph.D. degree in applied sciences from the Vrije Universiteit Brussel (VUB), Brussel, Belgium. He currently holds a professorship with the Department of Electronics and Informatics, VUB. He is a member of the scientific staff with the Interdisciplinary Institute for Broadband Technology, Ghent, Belgium. Additionally, since 1995, he has also been affiliated to the Interuniversity Microelectronics Institute, Leuven, Belgium, as a Scientific Collaborator. Since 2010, he has become a member of the board of councilors of the same institute. He is the author of over 200 papers in journals and conference proceedings. He is the coeditor of the books, The JPEG 2000 Suite (West Sussex, U.K.: Wiley, 2009) and Optical and Digital Image Processing (West Sussex, U.K.: Wiley, 2011). He is a holder of several patents and has contributed to several standardization processes. His research interests include multidimensional signal processing encompassing the representation, communication, security, and rendering of these signals while especially focusing on cross-disciplinary research. Dr. Schelkens is the Belgian Head of delegation for the ISO/IEC JPEG standardization committee, the Editor/Chair of part 10 of JPEG 2000: Extensions for Three-Dimensional Data, and the PR Chair of the JPEG committee. He is a member of SPIE and ACM and is currently the Belgian EURASIP Liaison Officer. In 2011, Peter Schelkens acted as General Co-Chair of IEEE International Conference on Image Processing and the Workshop on Quality of Multimedia Experience.

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