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

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

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

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.

原文英語
頁(從 - 到)197-208
頁數12
期刊Neurocomputing
37
發行號1-4
DOIs
出版狀態已發佈 - 2001
對外發佈

ASJC Scopus subject areas

  • 電腦科學應用
  • 認知神經科學
  • 人工智慧

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