Most well-established and widely used color difference (CD) metrics are handcrafted and subjectcalibrated against uniformly colored patches, which do not generalize well to photographic images characterized by natural scene complexities. Constructing CD formulae for photo-graphic images is still an active research topic in imaging/illumination, vision science, and color science communities. In this paper, we aim to learn a deep CD metric for photographic images with four desirable properties. First, it well aligns with the observations in vision science that color and form are linked inextricably in visual cortical processing. Second, it is a proper metric in the mathematical sense. Third, it computes accurate CDs between photographic images, differing mainly in color appearances. Fourth, it is robust to mild geometric distortions (e.g., translation or due to parallax), which are often present in photographic images of the same scene captured by different digital cameras. We show that all four properties can be satisfied at once by learning a multi-scale autoregressive normalizing flow for feature transform, followed by the Euclidean distance which is linearly proportional to the human perceptual CD. Quantitative and qualitative experiments on the large-scale SPCD dataset demonstrate the promise of the learned CD metric. Source code is available at https://github.com/haoychen3/CD-Flow.

In this paper, we investigate the minimum energy of transmitting correlated sources over the Gaussian multiple-access channel. Compared to other works on joint source-channel coding, we consider the fundamental problem of the minimum transmission energy, where the source and channel bandwidths are not naturally matched. Different models of correlated sources are studied. We first treat lossy transmission of Gaussian sources, including multiterminal sources and CEO sources. We then consider lossless transmission of correlated binary sources. In all cases, we lower bound the minimum energy using a cut-set argument that couples transmission energy and the distortions for the Gaussian cases (or source entropy for the discrete case). For the achievable schemes, separate source and channel coding and uncoded transmission are studied as benchmarks. In addition, we show that hybrid digital/analog transmission achieves the best known energy efficiency.

We consider the problem of distributed compression of the difference Z = Y1- cY2of two jointly Gaussian sources Y1and Y2under an MSE distortion constraint D on Z. The rate region for this problem is unknown if the correlation coefficient ρ and the weighting factor c satisfy cρ > 0. Inspired by Ahlswede and Han's scheme for the problem of distributed compression of the modulo-2 sum of two binary sources, we first propose a hybrid random-structured coding scheme that is capable of saving the sum-rate over both the random quantize-and-bin (QB) coding scheme and Krithivasan and Pradhan's structured lattice coding scheme. The main idea is to use a random coding component in the first layer to adjust the source correlation so that the structured coding component in the second layer can be more efficient with the outputs from the first layer as decoder side information. We then provide a new sum-rate lower bound for the problem in hand by connecting it to the Gaussian two-terminal source coding problem with covariance matrix distortion constraint. Our lower bound not only improves existing bounds in many cases, but also allows us to prove sum-rate tightness of the QB scheme when c is either relatively small or large and D is larger than some threshold. Furthermore, our lower bound enables us to show that our new hybrid scheme performs within two b/s from the optimal sum-rate for all values of ρ, c, and D.

We study the minimum energy of sending Gaussian multiterminal sources over the Gaussian multiple access channel (MAC). Distributed transmitters observe Gaussian multiterminal sources and describe their observations to a central decoder, which desires to reconstruct the sources under MSE constraints. We first lower bound the minimum energy by a cut-set argument which couples the transmitted signals and reconstruction errors. For achievability, separate source-channel coding is first studied as a benchmark. We then find out the minimum energy that can be achieved uncoded transmission. A hybrid digital/analog scheme is proposed to achieve the best known energy performance.

This paper considers the quadratic Gaussian multiterminal (MT) source coding problem and provides a new sufficient condition for the Berger–Tung (BT) sum-rate bound to be tight. The converse proof utilizes a set of virtual remote sources given which the observed sources are block independent with a maximum block size of 2. The given MT source coding problem is then related to a set of two-terminal problems with matrix-distortion constraints, for which a new lower bound on the sum-rate is given. By formulating a convex optimization problem over all distortion matrices, a sufficient condition is derived for the optimal BT scheme to satisfy the subgradient-based Karush–Kuhn–Tucker condition. The subset of the quadratic Gaussian MT problem satisfying our new sufficient condition subsumes all previously known tight cases, and our proof technique opens a new direction for more general partial solutions.

We propose a density evolution based dirty-paper code design framework that combines trellis coded quantization with multi-level low-density parity-check (LDPC) codes. Unlike existing design techniques based on Gaussian approximation and EXIT charts, the proposed framework tracks the empirically collected log-likelihood ratio (LLR) distributions at each iteration, and employs density evolution and differential evolution algorithms to design each LDPC component code. In order for the approximated LLR distributions obtained using density evolution to better match the true LLR distributions, a novel decorrelator is added to the decoder to make channel LLRs and check node LLRs almost independent of each other. Simulation results show that at 1 bit per sample transmission rate, the dirty-paper codes designed using the proposed method operate within 0.53 dB of the theoretical limit, reducing the best known result (with a 0.58 dB gap to the limit) by 0.05 dB at the same complexity and block length, while this improvement can be as large as 0.21 dB (corresponding to a 0.37 dB gap to the limit) when we use higher complexity encoder/decoder with longer block length.

A network code is said to be reliable when all transmissions in the network are (deterministic) functions of the source messages; well-known examples include decode-forward for relay networks. It is said to be unreliable when transmissions depend on the noise realization at nodes; examples include compress-forward and amplify-forward. The deterministic capacity of a network is defined as the supremum of the rates achievable by reliable codes. In this paper we derive the deterministic capacity of some relay networks in the low power regime. The resulting energy per bit is then compared with the one achievable by arbitrary transmission.

In this paper, we show tightness of the Berger-Tung (BT) sum-rate bound for a new class of quadratic Gaussian multiterminal (MT) source coding problems dubbed bi-eigen equal-variance with equal distortion (BEEV-ED), where theL×Lsource covariance matrix has equal diagonal elements with two distinct eigenvalues, and theLtarget distortions are equal. LetK(KL) be the number of repetitions of the larger eigenvalue, the BEEV covariance structure allows us to connectKi.i.d. virtual Gaussian sources with theLgiven MT sources via anL×Ksemiorthogonal transform whose rows have equal Euclidean norm plus additive i.i.d. Gaussian noises, resulting in the two sets of sources being mutually conditional i.i.d. By relating the given MT source coding problem to a generalized Gaussian CEO problem with theKvirtual sources as remote sources and theLMT sources as observations, we obtain a lower bound on the MT sum-rate, and show its achievability by BT schemes under the equal distortion constraints. Our BEEV-ED class of quadratic Gaussian MT source coding problems subsumes both the positive-symmetric case considered by Wagner et al. and the negative-symmetric case. Other examples, including a subclass of sources with BE circulant symmetric covariance matrices and equal distortion constraints, are also provided to highlight tightness of the sum-rate bound.

This paper considers a distributed source coding (DSC) problem where L encoders observe noisy linear combinations of K correlated remote Gaussian sources, and separately transmit the compressed observations to the decoder to reconstruct the remote sources subject to a sum-distortion constraint. This DSC problem is referred to as the generalized Gaussian CEO problem since it can be viewed as a generalization of the quadratic Gaussian CEO problem where the number of remote source K=1. First, we provide a new outer region obtained using the entropy power inequality and an equivalent argument (in the sense of having the same rate-distortion region and Berger-Tung inner region) among a certain class of generalized Gaussian CEO problems. We then give two sufficient conditions for our new outer region to match the inner region achieved by Berger-Tung schemes, where the second matching condition implies that in the low-distortion regime, the Berger-Tung inner rate region is always tight, while in the high-distortion regime, the same region is tight if a certain condition holds. The sum-rate part of the outer region is also studied and shown to meet the Berger-Tung sum-rate upper bound under a certain condition, which is obtained using the Karush-Kuhn-Tucker conditions of the underlying convex semidefinite optimization problem, and is in general weaker than the aforesaid two for rate region tightness.

We consider the problem of distributed compression of the difference Z = Y1-cY2of two jointly Gaussian sources Y1and Y2(with positive correlation coefficient ρ and positive c) under an MSE distortion constraint D on Z. The rate region for this problem is unknown. We provide a new lower bound on the minimum sum-rate by utilizing the connection of the above problem with the two-terminal source coding problem with matrix-distortion constraint. Our lower bound not only improves existing bounds in many cases, but also allows us to prove sum-rate tightness of the Berger-Tung scheme when c is either relatively small or large and D is larger than some threshold. Furthermore, our lower bound enables us to show that the improved lattice-based scheme recently introduced in [1] (with the smallest achievable sum-rate) performs within 1.18 b/s from the optimal sum-rate for all values of ρ, c, and D.

This paper addresses multiterminal video coding with the help of a low-resolution depth camera. In this setup, the depth sequence, together with the high-resolution texture sequences, are collected, compressed and transmitted separately to the joint decoder in order to obtain more accurate depth information and consequently better rate-distortion performance. At the decoder end, side information (for Wyner-Ziv coding) is generated based on successive refinement of the decompressed low-resolution depth map and texture frame warped from other terminals. Experimental results show sum-rate savings with the depth camera than without for the same PSNR performance. Comparisons to simulcast and JMVM coding are also provided. Although the sum-rate gain of our multiterminal video coding scheme (with or without the depth camera) over simulcast is relatively small, this work is the first that incorporates depth camera in a multiterminal setting.

This work studies the sum-rate loss of quadratic Gaussian multiterminal source coding, i.e., the difference between the minimum sum-rates of distributed encoding and joint encoding (both with joint decoding) of correlated Gaussian sources subject to MSE distortion constraints on individual sources. It is shown that under the nondegraded assumption, i.e., all target distortions are simultaneously achievable by a Gaussian Berger-Tung scheme, the supremum of the sum-rate loss of distributed encoding over joint encoding ofLjointly Gaussian sources increases almost linearly in the number of sourcesL, with an asymptotic slope of 0.1083 bit per sample per source asLgoes to infinity. This result is obtained even though we currently do not have the full knowledge of the minimum sum-rate for the distributed encoding case. The main idea is to upper-bound the minimum sum-rate of multiterminal source coding by that achieved by parallel Gaussian test channels while lower-bounding the minimum sum-rate of joint encoding by a reverse water-filling solution to a relaxed joint encoding problem of the same set of Gaussian sources with a sum-distortion constraint (that equals the sum of the individual target distortions). We show that under the nondegraded assumption, the supremum difference between the upper bound for distributed encoding and the lower bound for joint encoding is achieved in the bi-eigen equal-variance with equal distortion case, in which both bounds are known to be tight.

We study the generalized quadratic Gaussian CEO problem where K jointly Gaussian remote sources are transformed and independently-Gaussian-corrupted to form L observations, which are separately compressed by L encoders while the decoder attempts to reconstruct the K remote sources subject to a sum-distortion constraint. We first give a sufficient condition for the existing inner and outer regions to coincide, and then show that the new condition contains more matching cases than Oohama's. The main novelty lies in the fact that rotating the remote sources via orthogonal transformation while keeping a constant observation covariance matrix and the same observation noises does not change the rate region. Further, by exploiting the relationship between the sum-rate bound of the generalized quadratic Gaussian CEO problem and that of the quadratic Gaussian multiterminal source coding problem, we obtain new cases of the latter with tight sum-rate bound.

This work studies the sum-rate loss of quadratic Gaussian multiterminal source coding, i.e., the difference between the minimum sum-rates of distributed encoding and joint encoding (both with joint decoding) of correlated Gaussian sources subject to MSE distortion constraints on individual sources. It is shown that under the non-degraded assumption, i.e., all target distortions are simultaneously achievable by a Berger-Tung scheme, the supremum of the sum-rate loss of distributed encoding over joint encoding of L jointly Gaussian sources increases almost linearly in the number of sources L, with an asymptotic slope of 0.1083 b/s per source as L goes to infinity. This result is obtained even though we currently do not have the full knowledge of the minimum sum-rate for the distributed encoding case. The main idea is to upper-bound the minimum sum-rate of multiterminal source coding by that achieved by parallel Gaussian test channels while lower-bounding the minimum sum-rate of joint encoding by a reverse water-filling solution to a relaxed joint encoding problem of the same set of Gaussian sources with a sum-distortion constraint (that equals the sum of the individual target distortions). We show that under the non-degraded assumption, the supremum difference between the upper bound for distributed encoding and the lower bound for joint encoding is achieved in the bi-eigen equal-variance with equal distortion case, in which both bounds are known to be tight.

Symmetric distributed source coding has made great progress in the past several years. However, few attempts have been tried to use the symmetric distributed source coding schemes in real data compression scenarios. In this paper, with the inspiration of previous symmetric distributed source coding schemes, we first put forward a novel syndrome-based symmetric distributed coding scheme which can achieve the whole Slepian-Wolf rate region. Then we propose a general architecture for symmetric distributed multiview video schemes. Our experiments show very encouraging results. In the high-rate case, our scheme can achieve great rate saving from separate H.264 coding scheme at the same video quality.

Slepian-Wolf (SW) coding, which is concerned with separate near-lossless compression of correlated sources (with joint decoding), forms the basis of distributed source coding (DSC) and can be used to exploit the correlation among quantized sources in lossy DSC problems such as Wyner-Ziv (WZ) coding and multiterminal (MT) source coding. However, SW coding is in general lossy, especially at short block length, and practical implementation is not nearly as well understood as entropy coding. This paper studies distributed source coding without SW coding. We employ entropy coding (after quantization if necessary) at each encoder while relying on joint estimation at the decoder to exploit the source correlation. We start from the simple lossless case before giving single-letter characterizations of the rate-distortion function for WZ coding without SW compression, and achievable rate region for MT source coding without SW compression. Examples on the binary symmetric and quadratic Gaussian cases are given.

Whereas the theory and practice of two-terminal quadratic Gaussian multiterminal (MT) source coding is complete, the theory with more than two terminals is only partial, with the sum-rate limit only known in the symmetric case where all sources are positively symmetric and all target distortions equal. This paper proposes the first code design for quadratic Gaussian MT source coding in this symmetric setup. The aim is to approach corner points of the rate region via TCQ for quantization and LDPC codes for Slepian-Wolf compression. We provide high-rate analysis of our code design. Simulations with three and four terminals show a very small sum-rate loss.

Following recent works on the sum-rate of quadratic Gaussian multi-terminal coding of symmetric sources, this paper presents a practical three-terminal video coding scheme that saves the sum-rate over independent coding of three correlated video sequences. The first video sequence is coded by H.264/AVC, while the other two sequences are compressed sequentially using Wyner-Ziv coding with side information generated from the already decoded sequences. To improve the performance of Wyner-Ziv coding, a depth-map-based view interpolation approach and a novel soft-decision side information generation method are proposed. Experimental results show that our scheme can achieve a lower sum-rate than separate H.264/AVC coding. Comparison with JMVM joint encoding is also provided.

We propose a new technique, compress-spread forward (CSF), for high-performance wireless streaming from two base stations in parallel. CSF uses multiterminal source coding for efficient source compression and complete complementary sequences for error-free multiple access and synchronization. Our practical design shows significant performance gains due to spatial diversity and distributed source coding.

This paper considers trellis coded quantization (TCQ) and low-density parity-check (LDPC) codes for the quadratic Gaussian Wyner-Ziv coding problem. After TCQ of the source X, LDPC codes are used to implement Slepian-Wolf coding of the quantized source Q(X) with side information Y at the decoder. Assuming 256-state TCQ and ideal Slepian-Wolf coding in the sense of achieving the theoretical limit H(Q(X)|Y ), we experimentally show that Slepian-Wolf coded TCQ performs 0.2 dB away from the Wyner-Ziv distortion-rate function DWZ(R) at high rate. This result mirrors that of entropy-constrained TCQ in classic source coding of Gaussian sources. Furthermore, using 8,192-state TCQ and assuming ideal Slepian-Wolf coding, our simulations show that Slepian-Wolf coded TCQ performs only 0.1 dB away from DWZ(R) at high rate. These results establish the practical performance limit of Slepian-Wolf coded TCQ for quadratic Gaussian Wyner-Ziv coding. Practical designs give performance very close to the theoretical limit. For example, with 8,192-state TCQ, irregular LDPC codes for Slepian-Wolf coding and optimal non-linear estimation at the decoder, our performance gap to DWZ(R) is 0.20 dB, 0.22 dB, 0.30 dB, and 0.93 dB at 3.83 bit per sample (b/s), 1.83 b/s, 1.53 b/s, and 1.05 b/s, respectively. When 256-state 4-D trellis-coded vector quantization instead of TCQ is employed, the performance gap to DWZ(R) is 0.51 dB, 0.51 dB, 0.54 dB, and 0.80 dB at 2.04 b/s, 1.38 b/s, 1.0 b/s, and 0.5 b/s, respectively.

Following recent works on the rate region of the quadratic Gaussian two-terminal source coding problem and limit-approaching code designs, this paper examines multiterminal source coding of two correlated, i.e., stereo, video sequences to save the sum rate over independent coding of both sequences. Two multiterminal video coding schemes are proposed. In the first scheme, the left sequence of the stereo pair is coded by H.264/AVC and used at the joint decoder to facilitate Wyner-Ziv coding of the right video sequence. The first I-frame of the right sequence is successively coded by H.264/AVC Intracoding and Wyner-Ziv coding. An efficient stereo matching algorithm based on loopy belief propagation is then adopted at the decoder to produce pixel-level disparity maps between the corresponding frames of the two decoded video sequences on the fly. Based on the disparity maps, side information for both motion vectors and motion-compensated residual frames of the right sequence are generated at the decoder before Wyner-Ziv encoding. In the second scheme, source splitting is employed on top of classic and Wyner-Ziv coding for compression of both I-frames to allow flexible rate allocation between the two sequences. Experiments with both schemes on stereo video sequences using H.264/AVC, LDPC codes for Slepian-Wolf coding of the motion vectors, and scalar quantization in conjunction with LDPC codes for Wyner-Ziv coding of the residual coefficients give a slightly lower sum rate than separate H.264/AVC coding of both sequences at the same video quality.

This paper examines near-capacity dirty-paper code designs based on source–channel coding. We first point out that the performance loss in signal-to-noise ratio (SNR) in our code designs can be broken into the sum of the packing loss from channel coding and a modulo loss, which is a function of the granular loss from source coding and the target dirty-paper coding rate (or SNR). We then examine practical designs by combining trellis-coded quantization (TCQ) with both systematic and nonsystematic irregular repeat–accumulate (IRA) codes. Like previous approaches, we exploit the extrinsic information transfer (EXIT) chart technique for capacity-approaching IRA code design; but unlike previous approaches, we emphasize the role of strong source coding to achieve as much granular gain as possible using TCQ. Instead of systematic doping, we employ two relatively shifted TCQ codebooks, where the shift is optimized (via tuning the EXIT charts) to facilitate the IRA code design. Our designs synergistically combine TCQ with IRA codes so that they work together as well as they do individually. By bringing together TCQ (the best quantizer from the source coding community) and EXIT chart-based IRA code designs (the best from the channel coding community), we are able to approach the theoretical limit of dirty-paper coding. For example, at 0.25 bit per symbol (b/s), our best code design (with 2048-state TCQ) performs only 0.630 dB away from the Shannon capacity.

Data streaming from multiple base stations to a client is recognized as a robust technique for multimedia streaming. However the resulting transmission in parallel over wireless channels poses serious challenges, especially multiple access interference, multipath fading, noise effects and synchronization. Spread spectrum techniques seem the obvious choice to mitigate these effects, but at the cost of increased bandwidth requirements. This paper proposes a solution that exploits complete complementary spectrum spreading and data compression techniques jointly to resolve the communication challenges whilst ensuring efficient use of spectrum and acceptable bit error rate. Our proposed spreading scheme reduces the required transmission bandwidth by exploiting correlation among information present at multiple base stations. Results obtained show 1.75 Mchip/sec (or 25%) reduction in transmission rate, with only up to 6 dB loss in frequency-selective channel compared to a straightforward solution based solely on complete complementary spectrum spreading.

Distributed source coding is a promising enabling technology for sensor network applications. Symmetric distributed source coding can achieve the entire Slepian-Wolf rate region and offer sensor network applications a wide range of options to compress and transmit data. In this paper, we design a new symmetric distributed coding scheme that realize the benefits of both simplified code construction and bit incorrespondence handling. In particular, we propose a general scheme to deal with the bit incorrespondence problem, which is not specifically addressed by previous approaches. With the ability to deal with the bit incorrespondence in the code structure, it is more likely for our approach to be used in real sensor network environments.

Multiterminal (MT) source coding refers to separate lossy encoding and joint decoding of multiple correlated sources. Recently, the rate region of bothdirectandindirectMT source coding in the quadratic Gaussian setup with two encoders was determined. We are thus motivated to design practical MT source codes that can potentially achieve the entire rate region. In this paper, we present two practical MT coding schemes under the framework of Slepian–Wolf coded quantization (SWCQ) for both direct and indirect MT problems. The first,asymmetricSWCQ scheme relies on quantization and Wyner–Ziv coding, and it is implemented via source splitting to achieve any point on the sum–rate bound. In the second, conceptually simpler scheme,symmetricSWCQ, the two quantized sources are compressed using symmetric Slepian–Wolf coding via a channel code partitioning technique that is capable of achieving any point on the Slepian–Wolf sum–rate bound. Our practical designs employ trellis-coded quantization and turbo/low-density parity-check (LDPC) codes for both asymmetric and symmetric Slepian–Wolf coding. Simulation results show a gap of only 0.139–0.194 bit per sample away from the sum–rate bound for both direct and indirect MT coding problems.

We propose a system for real-time multimedia multicast over heterogeneous wireless-wireline networks. The encoded source is transmitted from the base station over the wireless radio link to numerous Internet servers. Each server performs distributed source coding (as quantization followed by Slepian-Wolf coding) by exploiting mutual correlation among packets received at different servers. The resulting packets are forwarded to the clients for joint decoding. We provide an algorithm for optimal nonuniform scalar quantizers design at the server side that minimizes the required rate under the decoder bit error rate constraint. For scalable multimedia codes, we develop joint source-channel coding scheme which combines error-protection at the base station and distributed source coding at the servers. Our experimental results show significant performance improvements over conventional solutions due to spatial diversity and distributed source coding gains.

Following recent works on the rate region of the quadratic Gaussian two-terminal source coding problem and limit-approaching code designs, this paper examines multiterminal source coding of two correlated video sequences to save the sum rate over independent coding. Specifically, the first video sequence is coded by H.264 and used at the joint decoder to facilitate Wyner-Ziv coding of the second video sequence. The first I-frame of the right sequence is successively coded by H.264 and Slepian-Wolf coding. An efficient stereo matching algorithm based on loopy belief propagation is then adopted at the decoder to produce pixel-level disparity maps between the corresponding frames of the two decoded video sequences on the fly. Based on the disparity maps, side information for both motion vectors and motion-compensated residual frames of the second sequence are generated at the decoder before Wyner-Ziv encoding. Experimental results on stereo video sequences using H.264, LDPC codes for Slepian-Wolf coding of the motion vectors and scalar quantization in conjunction with LDPC codes for Wyner-Ziv coding of the residual coefficients show savings in terms of the sum-rate when compared to separate H.264 coding at the same video quality.

Real-time multimedia multicast over wireless networks is an exciting application that has generated a lot of interest recently. Its main challenge lies in the stringent bandwidth and time-delay requirements of real-time multimedia and severe impairments of the wireless channels. We develop a system for multimedia multicast over wireless-wireline networks, that leverages the knowledge on network information theory, multimedia processing, error control, and networking. In particular, the encoded multimedia data are broadcast to multiple Internet servers over a wireless channel. Each server merely compresses the signal it has received using distributed source coding. The receiver collects bitstreams from the servers before performing joint decoding. Due to spatial diversity gain and distributed source coding, our system significantly outperforms conventional solutions.

Multiterminal (MT) source coding refers to separate lossy encoding and joint decoding of multiple correlated sources. This paper presents two practical MT coding schemes under the same general framework of Slepian-Wolf coded quantization (SWCQ) for both direct and indirect quadratic Gaussian MT source coding problems with two encoders. The first asymmetric SWCQ scheme relies on quantization and Wyner-Ziv coding, and is implemented via source-splitting to achieve any point on the inner sum-rate bound for both direct and indirect MT coding problems. In the second symmetric SWCQ scheme, the two quantization outputs are compressed using multilevel symmetric Slepian-Wolf coding. This scheme is conceptually simpler and can potentially achieve most of the points on the inner sum-rate bound. Our practical designs employ trellis coded quantization, LDPC code based asymmetric Slepian-Wolf code, and arithmetic code and turbo code based symmetric Slepian-Wolf code. Simulation results show a gap of only 0.24-0.29 bit per sample away from the inner sum-rate bound for both direct and indirect MT coding problems.

Asymmetric code design for remote multiterminal source coding in the quadratic Gaussian case is presented in this paper. For remote multiterminal source coding of X, to achieve the minimum sum rate of the two independent encoders subject to a fidelity criterion d, theoretical bounds were derived independently. The main idea is to quantize the first observation Y/sub 1/ and apply Wyner-Ziv coding on Y/sub 2/ by using the quantized version of Y/sub 1/ as side information in an efficient asymmetric coding scheme. The practical code design gives results that are very close to the sum-rate bound.