A novel competitive learning technique for the design of variable-rate vector quantizers with reproduction vector training in the wavelet domain

Wen Jyi Hwang, Maw Rong Leou, Shih Chiang Liao, Chienmin Ou

研究成果: 雜誌貢獻文章

摘要

This paper presents a novel competitive learning algorithm for the design of variable-rate vector quantizers (VQs). The algorithm, termed variable-rate competitive learning (VRCL) algorithm, designs a VQ having minimum average distortion subject to a rate constraint. The VRCL performs the weight vector training in the wavelet domain so that required training time is short. In addition, the algorithm enjoys a better rate-distortion performance than that of other existing VQ design algorithms and competitive learning algorithms. The learning algorithm is also more insensitive to the selection of initial codewords as compared with existing design algorithms. Therefore, the VRCL algorithm can be an effective alternative to the existing variable-rate VQ design algorithms for the applications of signal compression.

原文英語
頁(從 - 到)1781-1789
頁數9
期刊IEICE Transactions on Information and Systems
E83-D
發行號9
出版狀態已發佈 - 2000 一月 1

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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