Iterative source-channel decoding with Markov random field source models

Jörg Kliewer, Norbert Goertz, Alfred Mertins

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


We propose a joint source-channel decoding approach for multidimensional correlated source signals. A Markov random field (MRF) source model is used which exemplarily considers the residual spatial correlations in an image signal after source encoding. Furthermore, the MRF parameters are selected via an analysis based on extrinsic information transfer charts. Due to the link between MRFs and the Gibbs distribution, the resulting soft-input soft-output (SISO) source decoder can be implemented with very low complexity. We prove that the inclusion of a high-rate block code after the quantization stage allows the MRF-based decoder to yield the maximum average extrinsic information. When channel codes are used for additional error protection the MRF-based SISO source decoder can be used as the outer constituent decoder in an iterative source-channel decoding scheme. Considering an example of a simple image transmission system we show that iterative decoding can be successfully employed for recovering the image data, especially when the channel is heavily corrupted.

Original languageEnglish (US)
Pages (from-to)3688-3701
Number of pages14
JournalIEEE Transactions on Signal Processing
Issue number10
StatePublished - Oct 2006
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


  • EXIT charts
  • Iterative source-channel decoding
  • Joint source-channel coding
  • Markov random fields


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