A novel entropy-constrained competitive learning algorithm for vector quantization

Wen Jyi Hwang*, Bo Yuan Ye, Shi Chiang Liao

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

9 引文 斯高帕斯(Scopus)

摘要

In this paper, we present a novel competitive learning algorithm for the design of a variable-rate vector quantizer (VQ). The algorithm, termed entropy-constrained competitive learning (ECCL) algorithm, can achieve a near-optimal performance subject to the average rate constraint. Simulation results show that, under the same average rate, the ECCL algorithm enjoys a better performance than that of the existing competitive learning algorithms. Moreover, the ECCL algorithm outperforms the entropy-constrained vector quantizer (ECVQ) (Chou et al., IEEE Trans. Acoust. Speech Signal Process. 37 (1989) 31-42) design algorithm under the same rate constraint and initial codewords. The ECCL algorithm is also 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(從 - 到)133-147
頁數15
期刊Neurocomputing
25
發行號1-3
DOIs
出版狀態已發佈 - 1999 4月
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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