A novel fuzzy entropy-constrained competitive learning algorithm for image coding

Wen-Jyi Hwang, Faa Jeng Lin, Shi Chiang Liao, Jeng Hsin Huang

Research output: Contribution to journalArticle

Abstract

A novel variable-rate vector quantizer (VQ) design algorithm using both fuzzy and competitive learning technique is presented. The algorithm enjoys better rate-distortion performance than that of other existing fuzzy clustering and competitive learning algorithms. In addition, the learning algorithm is less sensitive to the selection of initial reproduction vectors. Therefore, the algorithm can be an effective alternative to the existing variable-rate VQ algorithms for signal compression.

Original languageEnglish
Pages (from-to)197-208
Number of pages12
JournalNeurocomputing
Volume37
Issue number1-4
DOIs
Publication statusPublished - 2001 Jan 1

Fingerprint

Entropy
Image coding
Learning algorithms
Learning
Fuzzy clustering
Reproduction
Cluster Analysis

Keywords

  • Competitive learning
  • Fuzzy clustering
  • Image coding
  • Vector quantization

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

A novel fuzzy entropy-constrained competitive learning algorithm for image coding. / Hwang, Wen-Jyi; Lin, Faa Jeng; Liao, Shi Chiang; Huang, Jeng Hsin.

In: Neurocomputing, Vol. 37, No. 1-4, 01.01.2001, p. 197-208.

Research output: Contribution to journalArticle

Hwang, Wen-Jyi ; Lin, Faa Jeng ; Liao, Shi Chiang ; Huang, Jeng Hsin. / A novel fuzzy entropy-constrained competitive learning algorithm for image coding. In: Neurocomputing. 2001 ; Vol. 37, No. 1-4. pp. 197-208.
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