On iterative source-channel image decoding with Markov random field source models

Jörg Kliewer, Norbert Görtz, Alfred Mertins

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


In this paper we propose a novel iterative source-channel decoding approach for robust transmission of compressed still images over noisy communication channels. Besides the explicit redundancy introduced by channel encoding also implicit residual source redundancy is exploited for error protection. The source redundancy is modeled by a Markov random field (MRF) source model, which considers the residual spatial correlation after source encoding. The resulting MRF-based soft-input/soft-output source decoder is used as outer constituent decoder in the proposed iterative source-channel decoding scheme, where due to the link between MRFs and the Gibbs distribution, the source decoder can be implemented with very low complexity, We show that this iterative decoding scheme can be successfully employed for recovering the image data, especially when the channel is highly corrupted.

Original languageEnglish (US)
Pages (from-to)IV-661-IV-664
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 2004
Externally publishedYes
EventProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
Duration: May 17 2004May 21 2004

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'On iterative source-channel image decoding with Markov random field source models'. Together they form a unique fingerprint.

Cite this