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Distributed source coding (DSC) is an important problem in information theory and communication. DSC problems regard the compression of multiple correlated information sources that do not communicate with each other.〔("Distributed source coding for sensor networks" by Z. Xiong, A.D. Liveris, and S. Cheng )〕 By modeling the correlation between multiple sources at the decoder side together with channel codes, DSC is able to shift the computational complexity from encoder side to decoder side, therefore provide appropriate frameworks for applications with complexity-constrained sender, such as sensor networks and video/multimedia compression (see distributed video coding〔("Distributed video coding in wireless sensor networks" by Puri, R. Majumdar, A. Ishwar, P. Ramchandran, K. )〕). One of the main properties of distributed source coding is that the computational burden in encoders is shifted to the joint decoder. ==History== In 1973, David Slepian and Jack Keil Wolf proposed the information theoretical lossless compression bound on distributed compression of two correlated i.i.d. sources X and Y.〔("Noiseless coding of correlated information sources" by D. Slepian and J. Wolf )〕 After that, this bound was extended to cases with more than two sources by Thomas M. Cover in 1975,〔("A proof of the data compression theorem of Slepian and Wolf for ergodic sources" by T. Cover )〕 while the theoretical results in the lossy compression case are presented by Aaron D. Wyner and Jacob Ziv in 1976.〔("The rate-distortion function for source coding with side information at the decoder" by A. Wyner and J. Ziv )〕 Although the theorems on DSC were proposed on 1970s, it was after about 30 years that attempts were started for practical techniques, based on the idea that DSC is closely related to channel coding proposed in 1974 by Aaron D. Wyner.〔("Recent results in Shannon theory" by A. D. Wyner )〕 The asymmetric DSC problem was addressed by S. S. Pradhan and K. Ramchandran in 1999, which focused on statistically dependent binary and Gaussian sources and used scalar and trellis coset constructions to solve the problem.〔("Distributed source coding using syndromes (DISCUS): design and construction" by S. S. Pradhan and K. Ramchandran )〕 They further extended the work into the symmetric DSC case.〔("Distributed source coding: symmetric rates and applications to sensor networks" by S. S. Pradhan and K. Ramchandran )〕 Syndrome decoding technology was first used in distributed source coding by the DISCUS system of SS Pradhan and K Ramachandran (Distributed Source Coding Using Syndromes).〔 They compress binary block data from one source into syndromes and transmit data from the other source uncompressed as side information. This kind of DSC scheme achieves asymmetric compression rates per source and results in ''asymmetric'' DSC. This asymmetric DSC scheme can be easily extended to the case of more than two correlated information sources. There are also some DSC schemes that use parity bits rather than syndrome bits. The correlation between two sources in DSC has been modeled as a virtual channel which is usually referred as a binary symmetric channel.〔("Distributed code constructions for the entire Slepian–Wolf rate region for arbitrarily correlated sources" by Schonberg, D. Ramchandran, K. Pradhan, S.S. )〕〔("Generalized coset codes for distributed binning" by Pradhan, S.S. Ramchandran, K. )〕 Starting from DISCUS, DSC has attracted significant research activity and more sophisticated channel coding techniques have been adopted into DSC frameworks, such as Turbo Code, LDPC Code, and so on. Similar to the previous lossless coding framework based on Slepian–Wolf theorem, efforts have been taken on lossy cases based on the Wyner–Ziv theorem. Theoretical results on quantizer designs was provided by R. Zamir and S. Shamai,〔("Nested linear/lattice codes for Wyner–Ziv encoding" by R. Zamir and S. Shamai )〕 while different frameworks have been proposed based on this result, including a nested lattice quantizer and a trellis-coded quantizer. Moreover, DSC has been used in video compression for applications which require low complexity video encoding, such as sensor networks, multiview video camcorders, and so on.〔("Distributed Video Coding" by B. Girod, etc. )〕 With deterministic and probabilistic discussions of correlation model of two correlated information sources, DSC schemes with more general compressed rates have been developed.〔("On code design for the Slepian–Wolf problem and lossless multiterminal networks" by Stankovic, V. Liveris, A.D. Zixiang Xiong Georghiades, C.N. )〕〔("A general and optimal framework to achieve the entire rate region for Slepian–Wolf coding" by P. Tan and J. Li )〕〔("Distributed source coding using short to moderate length rate-compatible LDPC codes: the entire Slepian–Wolf rate region" by Sartipi, M. Fekri, F. )〕 In these ''non-asymmetric'' schemes, both of two correlated sources are compressed. Under a certain deterministic assumption of correlation between information sources, a DSC framework in which any number of information sources can be compressed in a distributed way has been demonstrated by X. Cao and M. Kuijper.〔("A distributed source coding framework for multiple sources" by Xiaomin Cao and Kuijper, M. )〕 This method performs non-asymmetric compression with flexible rates for each source, achieving the same overall compression rate as repeatedly applying asymmetric DSC for more than two sources. Then, by investigating the unique connection between syndromes and complementary codewords of linear codes, they have translated the major steps of DSC joint decoding into syndrome decoding followed by channel encoding via a linear block code and also via its complement code,〔()"Distributed Source Coding via Linear Block Codes: A General Framework for Multiple Sources" by Xiaomin Cao and Kuijper, M.〕 which theoretically illustrated a method of assembling a DSC joint decoder from linear code encoders and decoders. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「distributed source coding」の詳細全文を読む スポンサード リンク
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