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Wyner-Ziv-based multiview video coding

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2008年第6期18卷 713-724页

作者： Guo, Xun Lu, Yan Wu, Feng Zhao, Debin Gao, Wen MediaTek Inc Beijing 100080 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Microsoft Res Asia Beijing 100080 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100080 Peoples R China

Utilizing video correlations among views would definitely improve multiview video compression in terms of coding efficiency, which usually requests an expensive system to collect videos from different cameras and jointly compress them. Thanks to recent developments on distributed video coding, this paper proposes a new multiview video coding scheme based on Wyner-Ziv (WZ) coding technique, in which the complicated temporal and interview correlation exploration process is shifted from the encoder side to the decoder side so that broadband raw data traffic and high intensive computation for jointly encoding can be avoided. At the encoder side, a wavelet-based WZ scheme is proposed to compress video of every camera. Furthermore, in order to better utilize correlation in wavelet domain, all coefficients are organized as that done in SPIHT bit plane by bit plane. At the decoder side, a more flexible prediction technique that can jointly utilize temporal and view correlations is proposed to generate side information. Finally, experimental results show the proposed scheme significantly outperforms the conventional intra-frame coding for better random access and is even close to the inter-frame coding for better efficiency. Furthermore, compressed data is much robust when it is transmitted over an error-prone channel.

关键词： distributed source coding multiview video coding (MVC) wavelet Wyner-Ziv (WZ) video coding

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distributed Lossless coding Techniques for Hyperspectral Images

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2015年第6期9卷 977-989页

作者： Zhang, Jinlei Li, Houqiang Chen, Chang Wen Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Anhui Peoples R China SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

In this paper, we present a novel distributed coding scheme for lossless, progressive and low complexity compression of hyperspectral images. Hyperspectral images have several unique requirements that are vastly different from consumer images. Among them, lossless compression, progressive transmission, and low complexity onboard processing are three most prominent ones. To satisfy these requirements, we design a distributed coding scheme that shifts the complexity of data decorrelation to the decoder side to achieve lightweight onboard processing after image acquisition. At the encoder, the images are subsampled in order to facilitate successive encoding and progressive transmission. At the decoder, we generate the side information with adaptive region-based predictor by taking full advantage of the decoded subsampled images and previously decoded neighboring bands based on the assumptions that the objects appearing in different bands are highly correlated. The proposed progressive transmission via subsampling enables the spectral correlation to be refined successively, resulting in gradually improved decoding performance of higher-resolution layers as more sub-images are decoded. Experimental results on the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data demonstrate that the proposed scheme is able to achieve competitive compression performance comparing with the-state-of-the-art 3D schemes, including existing distributed source coding (DSC) schemes. The proposed scheme has even lower encoding complexity than that of the conventional 2D schemes.

关键词： Lossless compression distributed source coding hyperspectral images low complexity encoding

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Generalized Independent Component Analysis Over Finite Alphabets

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IEEE TRANSACTIONS ON INFORMATION THEORY 2016年第2期62卷 1038-1053页

作者： Painsky, Amichai Rosset, Saharon Feder, Meir Tel Aviv Univ Dept Stat IL-6997801 Tel Aviv Israel Tel Aviv Univ Dept Elect Engn IL-6997801 Tel Aviv Israel

Independent component analysis (ICA) is a statistical method for transforming an observable multi-dimensional random vector into components that are as statistically independent as possible from each other. Usually, the ICA framework assumes a model according to which the observations are generated (such as a linear transformation with additive noise). ICA over finite fields is a special case of ICA in which both the observations and the independent components are over a finite alphabet. In this paper, we consider a generalization of this framework in which an observation vector is decomposed to its independent components (as much as possible) with no prior assumption on the way it was generated. This generalization is also known as Barlow's minimal redundancy representation problem and is considered an open problem. We propose several theorems and show that this hard problem can be accurately solved with a branch and bound search tree algorithm, or tightly approximated with a series of linear problems. Our contribution provides the first efficient set of solutions to Barlow's problem. The minimal redundancy representation (also known as factorial code) has many applications, mainly in the fields of neural networks and deep learning. The binary ICA is also shown to have applications in several domains, including medical diagnosis, multi-cluster assignment, network tomography, and internet resource management. In this paper, we show that this formulation further applies to multiple disciplines in source coding, such as predictive coding, distributed source coding, and coding of large alphabet sources.

关键词： Independent component analysis BICA ICA over galois field blind source separation minimal redundancy representation minimum entropy codes factorial codes predictive coding distributed source coding neural networks

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Low-Density Graph Codes That Are Optimal for Binning and coding With Side Information

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IEEE TRANSACTIONS ON INFORMATION THEORY 2009年第3期55卷 1061-1079页

作者： Wainwright, Martin J. Martinian, Emin Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Tilda Consulting Boston MA 02139 USA

In this paper, we describe and analyze the source and channel coding properties of a class of sparse graphical codes based on compounding a low-density generator matrix (LDGM) code with a low-density parity-check (LDPC) code. Our first pair of theorems establishes that there exist codes from this ensemble, with all degrees remaining bounded independently of block length, that are simultaneously optimal for both channel coding and source coding with binary data when encoding and decoding are performed optimally. More precisely, in the context of lossy compression, we prove that finite-degree constructions can achieve any pair (R, D) on the rate-distortion curve of the binary symmetric source. In the context of channel coding, we prove that the same finite-degree codes can achieve any pair (C, p) on the capacity-noise curve of the binary symmetric channel (BSC). Next, we show that our compound construction has a nested structure that can be exploited to achieve the Wyner-Ziv bound for source coding with side information (SCSI), as well as the Gelfand-Pinsker bound for channel coding with side information (CCSI). Although the results described here are based on optimal encoding and decoding, the proposed graphical codes have sparse structure and high girth that renders them well suited to message passing and other efficient decoding procedures.

关键词： Channel coding coding with side information distributed source coding Gelfand-Pinsker problem graphical codes information embedding low-density generator matrix code (LDGM) low-density parity-check code (LDPC) source coding weight enumerator Wyner-Ziv problem

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distributed coding of three binary and Gaussian correlated sources using punctured Turbo Codes

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SIGNAL PROCESSING 2006年第11期86卷 3131-3149页

作者： Lajnef, Khaled Guillemot, Christine Siohan, Pierre IRISA INRIA F-35042 Rennes France France Telecom R&D Div F-35512 Rennes France

In this paper, we address the problem of distributed coding of three correlated memoryless binary and Gaussian sources using punctured Turbo Codes. We first revisit the problem of coding with side information of two binary and Gaussian sources. The problem of coding continuous-valued Gaussian sources with partial side information is considered. The impact of the distortion induced by the quantization of the side information on the performance of the coding system is analyzed. Theoretical bounds as well as practical coding performances of source coding with partial side information are given. This leads to the derivation of appropriate settings for the problem of distributed coding of three sources. Rate bounds for the problems of coding with side information of three binary or Gaussian sources are derived. Practical solutions based on punctured Turbo Codes are provided. Simulation results are presented and analyzed for different amounts of correlation. (c) 2006 Elsevier B.V. All rights reserved.

关键词： data compression source coding with side information distributed source coding Slepian-Wolf coding Wyner-Ziv coding punctured Turbo Codes

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Vector Gaussian CEO Problem Under Logarithmic Loss and Applications

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IEEE TRANSACTIONS ON INFORMATION THEORY 2020年第7期66卷 4183-4202页

作者： Ugur, Yigit Aguerri, Inaki Estella Zaidi, Abdellatif Univ Paris Est Lab Informat Gaspard Monge F-77454 Champs Sur Marne France Huawei Technol Paris Res Ctr Math & Algorithm Sci Lab F-92100 Boulogne France Telephon Innovac Alpha Barcelona 08019 Spain

In this paper, we study the vector Gaussian Chief Executive Officer (CEO) problem under logarithmic loss distortion measure. Specifically, K >= 2 agents observe independently corrupted Gaussian noisy versions of a remote vector Gaussian source, and communicate independently with a decoder or CEO over rate-constrained noise-free links. The CEO also has its own Gaussian noisy observation of the source and wants to reconstruct the remote source to within some prescribed distortion level where the incurred distortion is measured under the logarithmic loss penalty criterion. We find an explicit characterization of the rate-distortion region of this model. The result can be seen as the counterpart to the vector Gaussian setting of that by Courtade-Weissman which provides the rate-distortion region of the model in the discrete memoryless setting. For the proof of this result, we obtain an outer bound by means of a technique that relies on the de Bruijn identity and the properties of Fisher information. The approach is similar to Ekrem-Ulukus outer bounding technique for the vector Gaussian CEO problem under quadratic distortion measure, for which it was there found generally non-tight;but it is shown here to yield a complete characterization of the region for the case of logarithmic loss measure. Also, we show that Gaussian test channels with time-sharing exhaust the Berger-Tung inner bound, which is optimal. Furthermore, application of our results allows us to find the complete solutions of two related problems: a quadratic vector Gaussian CEO problem with determinant constraint and the vector Gaussian distributed Information Bottleneck problem. Finally, we develop Blahut-Arimoto type algorithms that allow to compute numerically the regions provided in this paper, for both discrete and Gaussian models. With the known relevance of the logarithmic loss fidelity measure in the context of learning and prediction, the proposed algorithms may find usefulness in a variety of app

关键词： Loss measurement Distortion Rate-distortion Distortion measurement source coding Noise measurement Decoding Multiterminal source coding distributed source coding CEO problem rate-distortion region Berger-Tung inner bound logarithmic loss information bottleneck Blahut-Arimoto algorithm

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Layered Wyner-Ziv video coding

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2006年第12期15卷 3791-3803页

作者： Xu, Qian Xiong, Zixiang Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

Following recent theoretical works on successive Wyner-Ziv coding (WZC), we propose a practical layered Wyner-Ziv video coder using the DCT, nested scalar quantization, and irregular LDPC code based Slepian-Wolf coding (or lossless source coding with side information at the decoder). Our main novelty is to use the base layer of a standard scalable video coder (e.g., MPEG-4/H.26L FGS or H.263+) as the decoder side information and perform layered WZC for quality enhancement. Similar to FGS coding, there is no performance difference between layered and monolithic WZC when the enhancement bitstream is generated in our proposed coder. Using an H.26L coded version as the base layer, experiments indicate that WZC gives slightly worse performance than FGS coding when the channel (for both the base and enhancement layers) is noiseless. However, when the channel is noisy, extensive simulations of video transmission over wireless networks conforming to the CDMA2000 1X standard show that H.26L base layer coding plus Wyner-Ziv enhancement layer coding are more robust against channel errors than H.26L FGS coding. These results demonstrate that layered Wyner-Ziv video coding is a promising new technique for video streaming over wireless networks.

关键词： distributed source coding layered coding MPEG-4/H.26L FGS coding Slepian-Wolf coding (SWC) wireless video Wyner-Ziv coding (WZC)

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Robust Uplink Communications over Fading Channels with Variable Backhaul Connectivity

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2013年第11期12卷 5788-5799页

作者： Karasik, Roy Simeone, Osvaldo Shamai (Shitz), Shlomo Technion Israel Inst Technol Fac Elect Engn IL-32000 Haifa Israel New Jersey Inst Technol Ctr Wireless Commun & Signal Proc Res Newark NJ 07102 USA

Two mobile users communicate with a central decoder via two base stations. Communication between the mobile users and the base stations takes place over a Gaussian interference channel with constant channel gains or quasi-static fading. Instead, the base stations are connected to the central decoder through orthogonal finite-capacity links, whose connectivity is subject to random fluctuations. There is only receive-side channel state information, and hence the mobile users are unaware of the channel state and of the backhaul connectivity state, while the base stations know the fading coefficients but are uncertain about the backhaul links' state. The base stations are oblivious to the mobile users' codebooks and employ compress-and-forward to relay information to the central decoder. Upper and lower bounds are derived on average achievable throughput with respect to the prior distribution of the fading coefficients and of the backhaul links' states. The lower bounds are obtained by proposing strategies that combine the broadcast coding approach and layered distributed compression techniques. The upper bound is obtained by assuming that all the nodes know the channel state. Numerical results confirm the advantages of the proposed approach with respect to conventional non-robust strategies in both scenarios with and without fading.

关键词： Broadcast coding distributed source coding robust communication fading limited backhaul multicell processing cloud radio access network

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Robust and Efficient distributed Compression for Cloud Radio Access Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2013年第2期62卷 692-703页

作者： Park, Seok-Hwan Simeone, Osvaldo Sahin, Onur Shamai (Shitz), Shlomo New Jersey Inst Technol Dept Elect & Comp Engn Ctr Wireless Commun & Signal Proc Res Newark NJ 07102 USA InterDigital Inc Melville NY 11747 USA Technion Israel Inst Technol Dept Elect Engn IL-32000 Haifa Israel

This paper studies distributed compression for the uplink of a cloud radio access network where multiple multiantenna base stations (BSs) are connected to a central unit, which is also referred to as a cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, distributed source coding strategies are potentially beneficial. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and they are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst case approach, and an algorithm is proposed that achieves a stationary point for the problem. Then, BS selection is addressed with the aim of reducing the number of active BSs, thus enhancing the energy efficiency of the network. An optimization problem is formulated in which compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed that is shown to converge to a locally optimal point. From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information. Moreover, the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.

关键词： Cloud radio access networks distributed source coding multicell processing

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Optimal Matching in Wireless Sensor Networks

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2007年第4期1卷 725-735页

作者： Roumy, Aline Gesbert, David INRIA F-35042 Rennes France Eurecom Inst F-06904 Sophia Antipolis France

We design a wireless sensor network (WSN) in terms of rate and power allocation in order to send without loss the data gathered by the nodes to a common sink. Correlation between the data and channel impairments dictate the constraints of the optimization problem. We further assume that the WSN uses off-the-shelf compression and channel coding algorithms. More precisely source and channel coding are separated and distributed source coding is performed by pairs of nodes. This raises the problem of optimally matching the nodes. We show that under all these constraints the optimal design (including rate/power allocation and matching) has polynomial complexity (in the number of nodes in the network). A closed form solution is given for the rate/power allocation, and the matching solution is readily interpreted. For noiseless channels, the optimization matches close nodes whereas, for noisy channels, there is a tradeoff between matching close nodes and matching nodes with different distances to the sink. This fact is illustrated by simulations based on empirical measures. We also show that the matching technique provides substantial gains in either storage capacity or power consumption for the WSN with regard to the case where the correlation between the nodes is not used.

关键词： distributed source coding power allocation rate allocation weighted matching wireless sensor network

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