Genetic fuzzy entropy-constrained vector quantization

Wen Jyi Hwang*, Ching Fung Chine, Sheng Lin Hong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


A novel variable-rate vector quantizer (VQ) design algorithm using both genetic and fuzzy clustering techniques is presented. The algorithm, termed genetic fuzzy entropy-constrained VQ (GFECVQ) design algorithm, has a superior rate-distortion performance than that of the existing variable-rate VQ design algorithms. The algorithm utilizes fuzzy clustering technique to enhance the rate-distortion performance for the VQ design. In addition, a novel genetic algorithm is employed to ensure the robustness of the performance against the selection of initial parameters. Simulation results demonstrate that the FECVQ can be an effective alternative for the design of variable-rate VQs.

Original languageEnglish
Pages (from-to)369-377
Number of pages9
JournalJournal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an
Issue number3
Publication statusPublished - 2001
Externally publishedYes


  • Fuzzy C-means algorithm
  • Fuzzy clustering
  • Image compression
  • Vector quantization

ASJC Scopus subject areas

  • General Engineering


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