On some new approaches to practical Slepian-Wolf compression inspired by channel coding

We introduce three new innovations for compression using LDPCs for the Slepian-Wolf problem. The first is a general iterative Slepian-Wolf decoding algorithm that incorporates the graphical structure of all the encoders and operates in a 'turbo-like' fashion. The second innovation introduces source-splitting to enable low-complexity pipelined implementations of Slepian-Wolf decoding at rates besides corner points of the Slepian-Wolf region. This innovation can also be applied to single-source block coding for reduced decoder complexity. The third approach is a linear programming relaxation to maximum-likelihood sequence decoding that exhibits the ML-certificate property. This can be used for decoding a single binary block-compressed source as well as decoding at vertex points for the binary Slepian-Wolf problem. All three of these innovations were motivated by recent analogous results in the channel coding domain.

[1]  Patrick Mitran,et al.  Coding for the Slepian-Wolf problem with turbo codes , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[2]  R. Urbanke,et al.  Asynchronous Slepian-Wolf coding via source-splitting , 1997, Proceedings of IEEE International Symposium on Information Theory.

[3]  M. Wainwright,et al.  Using Linear Programming to Decode Linear Codes , 2003 .

[4]  Bernd Girod,et al.  Compression with side information using turbo codes , 2002, Proceedings DCC 2002. Data Compression Conference.

[5]  Wei Zhong,et al.  Compression of correlated sources using LDPC codes , 2003, Data Compression Conference, 2003. Proceedings. DCC 2003.

[6]  Philip A. Whiting,et al.  Rate-splitting multiple access for discrete memoryless channels , 2001, IEEE Trans. Inf. Theory.

[7]  Ying Zhao,et al.  Compression of correlated binary sources using turbo codes , 2001, IEEE Communications Letters.

[8]  G. Forney,et al.  Codes on graphs: normal realizations , 2000, 2000 IEEE International Symposium on Information Theory (Cat. No.00CH37060).

[9]  Jon Feldman,et al.  Decoding error-correcting codes via linear programming , 2003 .

[10]  Zixiang Xiong,et al.  Compression of binary sources with side information at the decoder using LDPC codes , 2002, IEEE Communications Letters.

[11]  Imre Csiszár.  Linear codes for sources and source networks: Error exponents, universal coding , 1982, IEEE Trans. Inf. Theory.

[12]  Martin J. Wainwright,et al.  Using linear programming to Decode Binary linear codes , 2005, IEEE Transactions on Information Theory.

[13]  Zixiang Xiong,et al.  Distributed compression of binary sources using conventional parallel and serial concatenated convolutional codes , 2003, Data Compression Conference, 2003. Proceedings. DCC 2003.

[14]  Kannan Ramchandran,et al.  Distributed source coding using syndromes (DISCUS): design and construction , 2003, IEEE Trans. Inf. Theory.

[15]  Jack K. Wolf,et al.  Noiseless coding of correlated information sources , 1973, IEEE Trans. Inf. Theory.