A Class of Distortionless Codes Designed by Bayes Decision Theory

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32 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

Keywords

  • Bayes principle
  • Source coding
  • and nonpredictive coding
  • decision theory. universal code
  • mean-code length
  • minimax principle
  • predictive coding

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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