A class of distortionless codes designed by Bayes decision theory

Toshiyasu Matsushima, Hiroshige Inazumi, Shigeichi Hirasawa

    Research output: Contribution to journalArticle

    29 Citations (Scopus)

    Abstract

    The problem of distortionless encoding when the parameters of the probabilistic model of a source are unknown is considered from a statistical decision theory point of view. A class of predictive and nonpredictive codes is proposed that are optimal within this framework. Specifically, it is shown that the codeword length of the proposed predictive code coincides with that of the proposed nonpredictive code for any source sequence. A bound for the redundancy for universal coding is given in terms of the supremum of the Bayes risk. If this supremum exists, then there exists a minimax code whose mean code length approaches it in the proposed class of codes, and the minimax code is given by the Bayes solution relative to the prior distribution of the source parameters that maximizes the Bayes risk.

    Original languageEnglish
    Pages (from-to)1288-1293
    Number of pages6
    JournalIEEE Transactions on Information Theory
    Volume37
    Issue number5
    DOIs
    Publication statusPublished - 1991 Sep

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    decision theory
    Decision theory
    redundancy
    Redundancy
    coding
    Statistical Models

    ASJC Scopus subject areas

    • Information Systems
    • Electrical and Electronic Engineering

    Cite this

    A class of distortionless codes designed by Bayes decision theory. / Matsushima, Toshiyasu; Inazumi, Hiroshige; Hirasawa, Shigeichi.

    In: IEEE Transactions on Information Theory, Vol. 37, No. 5, 09.1991, p. 1288-1293.

    Research output: Contribution to journalArticle

    Matsushima, Toshiyasu ; Inazumi, Hiroshige ; Hirasawa, Shigeichi. / A class of distortionless codes designed by Bayes decision theory. In: IEEE Transactions on Information Theory. 1991 ; Vol. 37, No. 5. pp. 1288-1293.
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