A general formula of rate-distortion functions for source coding with side information at many decoders

2012 ◽  
Author(s):  
Tetsunao Matsuta ◽  
Tomohiko Uyematsu
Keyword(s):  
General Formula ◽  
Source Coding ◽  
Side Information ◽  
Rate Distortion ◽  
Distortion Functions

scholarly journals Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 213 ◽  
Author(s):  
Yizhong Wang ◽  
Li Xie ◽  
Siyao Zhou ◽  
Mengzhen Wang ◽  
Jun Chen
Keyword(s):  
Mean Squared Error ◽  
Source Coding ◽  
Rate Distortion ◽  
Asymptotic Rate ◽  
Squared Error ◽  
Simple Characterization ◽  
Distortion Functions ◽  
Distortion Analysis ◽  
Gaussian Source ◽  
Multivariate Gaussian

Consider a symmetric multivariate Gaussian source with ℓ components, which are corrupted by independent and identically distributed Gaussian noises; these noisy components are compressed at a certain rate, and the compressed version is leveraged to reconstruct the source subject to a mean squared error distortion constraint. The rate-distortion analysis is performed for two scenarios: centralized encoding (where the noisy source components are jointly compressed) and distributed encoding (where the noisy source components are separately compressed). It is shown, among other things, that the gap between the rate-distortion functions associated with these two scenarios admits a simple characterization in the large ℓ limit.

An upper bound on the rate distortion function for source coding with partial side information at the decoder

1979 ◽  
Vol 25 (6) ◽  
pp. 664-666 ◽  
Author(s):  
T. Berger ◽  
K. Housewright ◽  
J. Omura ◽  
Suiyin Yung ◽  
J. Wolfowitz
Keyword(s):  
Upper Bound ◽  
Source Coding ◽  
Side Information ◽  
Rate Distortion ◽  
Distortion Function

scholarly journals Exponential Strong Converse for Successive Refinement with Causal Decoder Side Information

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 410
Author(s):  
Lin Zhou ◽  
Alfred Hero
Keyword(s):  
Current Problem ◽  
Source Coding ◽  
Side Information ◽  
Rate Distortion ◽  
Converse Theorem ◽  
Successive Refinement ◽  
Strong Converse Theorem ◽  
Variational Form ◽  
Strong Converse ◽  
Special Case

We consider the k-user successive refinement problem with causal decoder side information and derive an exponential strong converse theorem. The rate-distortion region for the problem can be derived as a straightforward extension of the two-user case by Maor and Merhav (2008). We show that for any rate-distortion tuple outside the rate-distortion region of the k-user successive refinement problem with causal decoder side information, the joint excess-distortion probability approaches one exponentially fast. Our proof follows by judiciously adapting the recently proposed strong converse technique by Oohama using the information spectrum method, the variational form of the rate-distortion region and Hölder’s inequality. The lossy source coding problem with causal decoder side information considered by El Gamal and Weissman is a special case ( k = 1 ) of the current problem. Therefore, the exponential strong converse theorem for the El Gamal and Weissman problem follows as a corollary of our result.

scholarly journals Low-Complexity Compression Algorithm for Hyperspectral Images Based on Distributed Source Coding

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Yongjian Nian ◽  
Mi He ◽  
Jianwei Wan
Keyword(s):  
Source Coding ◽  
Side Information ◽  
Rate Distortion ◽  
Lossless Compression ◽  
Low Complexity ◽  
Distributed Source Coding ◽  
Compression Algorithm ◽  
Hyperspectral Images ◽  
Multilinear Regression ◽  
Distributed Source

A low-complexity compression algorithm for hyperspectral images based on distributed source coding (DSC) is proposed in this paper. The proposed distributed compression algorithm can realize both lossless and lossy compression, which is implemented by performing scalar quantization strategy on the original hyperspectral images followed by distributed lossless compression. Multilinear regression model is introduced for distributed lossless compression in order to improve the quality of side information. Optimal quantized step is determined according to the restriction of the correct DSC decoding, which makes the proposed algorithm achieve near lossless compression. Moreover, an effective rate distortion algorithm is introduced for the proposed algorithm to achieve low bit rate. Experimental results show that the compression performance of the proposed algorithm is competitive with that of the state-of-the-art compression algorithms for hyperspectral images.

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