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

Moon Seo Park, David Jonathan Miller

    Research output: Contribution to conferencePaper

    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 - Dec 1 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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    Park, M. S., & Miller, D. J. (1997). Image decoding over noisy channels using minimum mean-squared estimation and a Markov mesh. 594-597. Paper presented at Proceedings of the 1997 International Conference on Image Processing. Part 2 (of 3), Santa Barbara, CA, USA, .