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

Moon Seo Park, David J. Miller

    Research output: Contribution to journalArticle

    15 Scopus citations

    Abstract

    Joint source-channel (JSC) decoding based on residual source redundancy is a technique for providing channel robustness to quantized data. Previous work assumed a model equivalent to viewing the encoder/noisy channel tandem as a discrete hidden Markov model (HMM) with transmitted indices the hidden states. Here, we generalize this HMM-based (1-D) approach for images, using the more powerful hidden Markov mesh random field (HMMRF) model. While previous state estimation methods for HMMRF's base estimates on only a causal subset of the observed data, our new method uses both causal and anticausal subsets. For JSC-based image decoding, the new method provides significant benefits over several competing techniques.

    Original languageEnglish (US)
    Pages (from-to)863-867
    Number of pages5
    JournalIEEE Transactions on Image Processing
    Volume8
    Issue number6
    DOIs
    StatePublished - Jan 1 1999

    All Science Journal Classification (ASJC) codes

    • Software
    • Computer Graphics and Computer-Aided Design

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