Entropy-constrained vector quantizer design algorithm using competitive learning technique

Wen Jyi Hwang, Maw Rong Leou, Bo Yuan Ye, Shi Chiang Liao

研究成果: 會議貢獻類型

摘要

A novel full-search variable-rate vector quantizer (VQ) design algorithm using competitive learning technique is presented. The algorithm, termed entropy-constrained competitive learning (ECCL) algorithm, can design a VQ having minimum average distortion subject to a rate constraint. The ECCL algorithm enjoys a better rate-distortion performance than that of the existing competitive learning algorithms. Moreover, the ECCL algorithm outperforms the entropy-constrained vector quantizer (ECVQ) design algorithm subject to the same rate and storage size constraints. In addition, the learning algorithm is more insensitive to the selection of initial codewords as compared with the ECVQ algorithm. Therefore, the ECCL algorithm can be an effective alternative to the existing variable-rate VQ design algorithms for the applications of signal compression.

原文英語
頁面1715-1721
頁數7
出版狀態已發佈 - 1998 十二月 1
事件Proceedings of the IEEE GLOBECOM 1998 - The Bridge to the Global Integration - Sydney, NSW, Aust
持續時間: 1998 十一月 81998 十一月 12

會議

會議Proceedings of the IEEE GLOBECOM 1998 - The Bridge to the Global Integration
城市Sydney, NSW, Aust
期間98/11/898/11/12

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Global and Planetary Change

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  • 引用此

    Hwang, W. J., Leou, M. R., Ye, B. Y., & Liao, S. C. (1998). Entropy-constrained vector quantizer design algorithm using competitive learning technique. 1715-1721. 論文發表於 Proceedings of the IEEE GLOBECOM 1998 - The Bridge to the Global Integration, Sydney, NSW, Aust, .