Entropy-constrained vector quantizer design algorithm using competitive learning technique

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

Entropy-constrained vector quantizer design algorithm using competitive learning technique

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

Department of Computer science & Information Engineering

Research output: Contribution to conference › Paper

Abstract

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.

Original language	English
Pages	1715-1721
Number of pages	7
Publication status	Published - 1998 Dec 1
Event	Proceedings of the IEEE GLOBECOM 1998 - The Bridge to the Global Integration - Sydney, NSW, Aust Duration: 1998 Nov 8 → 1998 Nov 12

Conference

Conference	Proceedings of the IEEE GLOBECOM 1998 - The Bridge to the Global Integration
City	Sydney, NSW, Aust
Period	98/11/8 → 98/11/12

ASJC Scopus subject areas

Electrical and Electronic Engineering
Global and Planetary Change

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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. Paper presented at Proceedings of the IEEE GLOBECOM 1998 - The Bridge to the Global Integration, Sydney, NSW, Aust, .

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abstract = "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.",

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N2 - 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.

AB - 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.

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