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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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
Issue number1-4
Publication statusPublished - 2001
Externally publishedYes


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

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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