Adaptive Entropy-Coded Predictive Vector Quantization of Images

James W. Modestino, Yong Han Kim

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

The authors consider 2-D predictive vector quantization (PVQ) of images subject to an entropy constraint and demonstrate the substantial performance improvements over existing unconstrained approaches. They describe a simple adaptive buffer-instrumented implementation of this 2-D entropy-coded PVQ scheme which can accommodate the associated variable-length entropy coding while completely eliminating buffer overflow/underflow problems at the expense of only a slight degradation in performance. This scheme, called 2-D PVQ/AECQ (adaptive entropy-coded quantization), is shown to result in excellent rate-distortion performance and impressive quality reconstructions of real-world images. Indeed, the real-world coding results shown demonstrate little distortion at rates as low as 0.5 b/pixel.

Original languageEnglish (US)
Pages (from-to)633-644
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume40
Issue number3
DOIs
StatePublished - Mar 1992

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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