Image decoding over noisy channels using minimum mean-squared estimation and a Markov mesh

Moon Seo Park, David Jonathan Miller

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

Recently, we developed a sequence-based minimum mean-squared error (MMSE) estimator for decoding quantized data transmitted over noisy channels. The method effectively views the encoder and noisy channel tandem as a discrete hidden Markov model (HMM), with transmitted indices the unknown states and received indices the observable symbols. Here, we extend this 1D approach to images, using a Markov mesh random field to model the encoded image. Our decoder is based on an approximate Forward/Backward algorithm for calculating pixel `label probabilities' in Markov meshes which may also have application to image labeling and segmentation. For a DPCM-based image coding system and a high error-rate channel, the new decoder obtains significant performance gains, both objective and visually discernable, over the standard decoder, as well as over several other competing techniques.

Original languageEnglish (US)
Pages594-597
Number of pages4
StatePublished - 1997
EventProceedings of the 1997 International Conference on Image Processing. Part 2 (of 3) - Santa Barbara, CA, USA
Duration: Oct 26 1997Oct 29 1997

Other

OtherProceedings of the 1997 International Conference on Image Processing. Part 2 (of 3)
CitySanta Barbara, CA, USA
Period10/26/9710/29/97

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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