Distributed source coding

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. 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). One of the main properties of distributed source coding is that the computational burden in encoders is shifted to the joint decoder. 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. 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). One of the main properties of distributed source coding is that the computational burden in encoders is shifted to the joint decoder. 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. After that, this bound was extended to cases with more than two sources by Thomas M. Cover in 1975, while the theoretical results in the lossy compression case are presented by Aaron D. Wyner and Jacob Ziv in 1976. 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. 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. They further extended the work into the symmetric DSC case. 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. 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, 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. With deterministic and probabilistic discussions of correlation model of two correlated information sources, DSC schemes with more general compressed rates have been developed. In these non-asymmetric schemes, both of two correlated sources are compressed.