A fuzzy entropy-constrained vector quantizer design algorithm and its applications to image coding

Wen Jyi Hwang, Sheng Lin Hong

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this paper, a novel variable-rate vector quantizer (VQ) design algorithm using fuzzy clustering technique is presented. The algorithm, termed fuzzy entropy-constrained VQ (FECVQ) design algorithm, has a better rate-distortion performance than that of the usual entropy-constrained VQ (ECVQ) algorithm for variable-rate VQ design. When performing the fuzzy clustering, the FECVQ algorithm considers both the usual squared-distance measure, and the length of channel index associated with each codeword so that the average rate of the VQ can be controlled. In addition, the membership function for achieving the optimal clustering for the design of FECVQ are derived. Simulation results demonstrate that the FECVQ can be an effective alternative for the design of variable-rate VQs.

Original languageEnglish
Pages (from-to)1109-1116
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE82-A
Issue number6
Publication statusPublished - 1999 Jan 1

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Keywords

  • Fuzzy c-means algorithm
  • Fuzzy clustering
  • Image compression
  • Image processing
  • Vector quantization

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

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