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Handbook on Array Processing and Sensor Networks by K. J. Ray Liu, Simon Haykin

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images CHAPTER 19

Distributed Source Coding

Zixiang Xiong1, Angelos D. Liveris, and Yang Yang

1Department of ECE, Texas A&M University, College Station, Texas

19.1 INTRODUCTION

In many emerging applications (e.g., distributed sensor networks), multiple correlated sources need to be separately compressed at distributed terminals before being transmitted to a central unit. Due to complexity and/or power constraints, the transmitters are often not allowed to communicate with each other. This gives rise to the problem of distributed source coding (DSC).

The foundation of DSC was laid by Slepian and Wolf in their 1973 study [1], which considered separate lossless compression of two correlated sources and showed the surprising result that separate encoding (with joint decoding) suffers no rate loss when compared to joint encoding. This seminal work (on Slepian–Wolf coding [1]) was subsequently extended to other DSC scenarios.

In 1976, Wyner and Ziv [2] extended one special case of Slepian–Wolf (SW) coding, namely, lossless source coding with decoder side information, to lossy source coding with decoder side information. Unlike SW coding, there is in general a rate loss associated with Wyner–Ziv (WZ) coding [2] when compared to lossy source coding with side information also available at the encoder. An exception occurs with quadratic Gaussian WZ coding when the source and side information are ...

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