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IEEE Information Theory Workshop

作者： Kipnis, Alon Rini, Stefano Goldsmith, Andrea J. Stanford Univ Dept Elect Engn Stanford CA 94305 USA Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 30050 Taiwan

ISBN: (纸本)9781467378529

The indirect source-coding problem in which a Bernoulli process is compressed in a lossy manner from its noisy observations is considered. These noisy observations are obtained by passing the source sequence through a binary symmetric channel so that the channel crossover probability controls the amount of information available about the source realization at the encoder. We use classic results in rate-distortion theory to compute the rate-distortion function for this model as a solution of an exponential equation. In addition, we derive an upper bound on the rate distortion which has a simple closed-form expression and investigate the coding scheme that attains it. These expressions capture precisely the expected behavior of the rate-distortion function: the noisier the source observations, the smaller the reduction in distortion obtained from increasing the compression rate.

关键词： Indirect rate distortion binary source binary symmetric channel

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Nonblock Coding of binary sources by Probabilistic Enumeration

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IEEE TRANSACTIONS ON INFORMATION THEORY 2011年第9期57卷 6170-6179页

作者： Teuhola, Jukka Univ Turku Dept Informat Technol SF-20500 Turku Finland

A simple and efficient nonblock coding scheme for binary sources is suggested. It uses a probability model to map any source string to a unique integer, and thereby defines an enumeration of all possible strings. Contrary to normal enumerative coding, the new method is noncombinatorial and operates sequentially, incrementing the code value symbol by symbol, by simple arithmetic. It is akin to arithmetic coding but does not use intervals. For memoryless sources, the redundancy per symbol is shown to be asymptotically less than p(3) bits, where p is the smaller of symbol probabilities. Especially, for integer-valued 1/p, the code is asymptotically optimal. These results are confirmed by both analysis and experiments with arbitrary-precision arithmetic. In a practical implementation, the source is partitioned into substrings enabling restricted-precision arithmetic. Thanks to subtle implicit coding, the additional redundancy is marginal. The source model can be also context-based, and even adaptivity can be incorporated. A peculiarity of the method is that decoding is done backwards (LIFO). Hence, for higher-order models, the string suffix, not prefix, is used as the context domain. The speed of the practical version is close to that of binary arithmetic coders.

关键词： Arithmetic coding binary source enumerative coding nonblock coding source coding

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Discriminatory Lossy source Coding: Side Information Privacy

Discriminatory Lossy Source Coding: Side Information Privacy

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54th Annual IEEE Global Telecommunications Conference (GLOBECOM)

作者： Tandon, Ravi Sankar, Lalitha Poor, H. Vincent Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)9781424492688

The Heegard-Berger problem models a situation in which encoding at a source has to account for two decoders, one with and one without correlated side information when the same information is not available at the encoder. The Heegard-Berger encoding scheme is proved to be rate-optimal even when an additional constraint on the privacy of side information is imposed at the uninformed decoder. The results are illustrated for a binary source with erasure side information and Hamming distortion, a result that is also of independent interest.

关键词： Decoding Heegard-Berger encoding scheme Heegard-Berger problem models IEEE Communications Society Indexes Markov processes Privacy source coding binary codes binary source discriminatory lossy source coding encoding side information privacy source coding uninformed decoder

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Optimal Rate Allocation in Multiterminal Compress-and-Estimate source Coding

Optimal Rate Allocation in Multiterminal Compress-and-Estima...

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IEEE Information Theory Workshop (ITW)

作者： Song, Ruiyang Rini, Stefano Kipnis, Alon Goldsmith, Andrea J. Tsinghua Univ Beijing Peoples R China Natl Chiao Tung Univ Hsinchu Taiwan Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781509010905

We consider a multiterminal source coding problem in which a source is estimated at a central processing unit from lossy-compressed remote observations. Each lossy-encoded observation is produced by a remote sensor. The sensor first obtains a noisy version of the source, then compresses this observation based on minimizing a local distortion measure that depends only on the marginal distribution of its observation. The central node, on the other hand, has knowledge of the joint distribution of the source and all the observations and produces the source estimate that minimizes a different distortion measure between the source and its reconstruction. In this paper, we investigate the problem of optimally choosing the rate of each lossy-compressed remote estimate so as to minimize the distortion at the central processor, subject to bound on the sum of the communication rate between the sensors and the central unit. We focus, in particular, on two models of practical relevance: the case of a Gaussian source observed in additive Gaussian noise and reconstructed under quadratic distortion, and the case of a binary source observed in bit-flipping noise and reconstructed under Hamming distortion. In both scenarios we show that there exist regimes under which having more remote encoders does not reduce the source distortion. In other words, having fewer, high-quality remote estimates provides a smaller distortion than having more, lower-quality estimates.

关键词： Remote source coding binary source Gaussian source binary symmetric channel CEO problem

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Multiterminal Compress-and-Estimate source Coding

Multiterminal Compress-and-Estimate Source Coding

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IEEE International Symposium on Information Theory (ISIT)

作者： Kipnis, Alon Rini, Stefano Goldsmith, Andrea J. Stanford Univ Dept Elect Engn Stanford CA 94305 USA Natl Chiao Tung Univ Dept Elect Engn Hsinchu 30010 Taiwan

ISBN: (纸本)9781509018062

We consider a multiterminal source coding problem in which a random source signal is estimated from encoded versions of multiple noisy observations. Each encoded version, however, is compressed so as to minimize a local distortion measure, defined only with respect to the distribution of the corresponding noisy observation. The original source is then estimated from these compressed noisy observations. We denote the minimal distortion under this coding scheme as the compress-and-estimate distortion-rate function (CE-DRF). We derive a single-letter expression for the CE-DRF in the case of an i.i.d source. We evaluate this expression for the case of a Gaussian source observed through multiple parallel AWGN channels and quadratic distortion and in the case of a non-uniform binary i.i.d source observed through multiple binary symmetric channels under Hamming distortion. For the case of a Gaussian source, we compare the performance for centralized encoding versus that of distributed encoding. In the centralized encoding scenario, when the code rates are sufficiently small, there is no loss of performance compared to the indirect source coding distortionrate function, whereas distributed encoding achieves distortion strictly larger then the optimal multiterminal source coding scheme. For the case of a binary source, we show that even with a single observation, the CE-DRF is strictly larger than that of indirect source coding.

关键词： Remote source coding Indirect source coding Gaussian source binary source Compress-and-estimate

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On rate-distortion with mixed types of side information

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IEEE TRANSACTIONS ON INFORMATION THEORY 2006年第4期52卷 1698-1705页

作者： Fleming, M Effros, M Mitchell Madison Grp New York NY 10022 USA CALTECH Dept Elect Engn Pasadena CA 91125 USA

In this correspondence, we consider rate-distortion examples in the presence of side information. For a system with some side information known at both the encoder and decoder, and some known only at the decoder, we evaluate the rate distortion function for both Gaussian and binary sources. While the Gaussian example is a straightforward generalization of the corresponding result by Wyner, the binary example proves more difficult and is solved using a multidimensional optimization approach. Lever-aging the insights gained from the binary example, we then solve the more complicated binary Heegard and Berger problem of decoding when side information may be present. The results demonstrate the existence of a new type of successive refinement in which the refinement information is decoded together with side information that is not available for the initial description.

关键词： binary source conditional Heegard-Berger multidimensional optimization successive refinement Wyner-Ziv

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Rate-Distortion Function for Finite Block Codes: Analysis of Symmetric binary Hamming Problem 21

Rate-Distortion Function for Finite Block Codes: Analysis of...

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21st National Conference on Communications (NCC)

作者： Avasarala, Srinivas Jana, Soumya Akella, Prabhakar Indian Inst Technol Hyderabad Andhra Pradesh India

ISBN: (纸本)9781479966196

Shannon's rate-distortion theory provides an asymptotic analysis, where delays are allowed to grow unbounded. In practice, real-time applications, such as video streaming and network storage, are subject to certain maximum delay. Accordingly, it is imperative to develop a finite-delay framework for analyzing the rate-distortion limit. In this backdrop, we propose an intuitive generalization of Shannon's asymptotic operational framework to finite block codes. In view of the extreme complexity of such framework, we obtain insight by specializing to the symmetric binary hamming problem. Even upon such specialization, the proposed framework is computationally so intensive that accurate evaluation of the finite-delay rate-distortion function is practical only upto a block length of three. In order to obtain further insight, we then propose a lower-complexity lower bound, based on the partition function of natural numbers, whose computation is practical upto a block length of six. Finally, using a simple combinatorial argument, we propose an upper hound to localize the desired rate-distortion function between our lower and upper hounds.

关键词： Finite block codes Rate-Distortion function Huffman coding binary source Hamming distortion

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Complexity and Rate-Distortion Tradeoff via Successive Refinement

Complexity and Rate-Distortion Tradeoff via Successive Refin...

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51st IEEE Annual Allerton Conference on Communication, Control, and Computing

作者： No, Albert Ingber, Amir Weissman, Tsachy Stanford Univ Dept Elect Engn Stanford CA 94305 USA

ISBN: (纸本)9781479934096

We demonstrate how successive refinement ideas can be used in point-to-point lossy compression problems in order to reduce complexity. We show two examples, the binary-Hamming and quadratic-Gaussian cases, in which a layered code construction results in a low complexity scheme that attains optimal performance. For example, when the number of layers grows with the block length n, we show how to design an O (n(log(n))) algorithm that asymptotically achieves the rate distortion bound. We then show that with the same scheme, used with a fixed number of layers, successive refinement is achieved in the classical sense, and at the same time the second order performance (i.e. dispersion) is also tight.

关键词： binary source complexity Gaussian source rate-distortion refined strong covering lemma source dispersion sparse regression code successive refinement

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On the Suboptimality of Linear Lossy Codes

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2008年第10期E91A卷 2868-2869页

作者： Kuzuoka, Shigeaki Wakayama Univ Dept Comp & Commun Sci Wakayama 6408510 Japan

This letter reveals that linear lossy codes cannot attain the rate-distortion function in general, even if the source is binary i.i.d. and the distortion is measured by the Hamming distortion measure.

关键词： binary source Hamming distortion linear code lossy source coding

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