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

Wen Jyi Hwang; Faa Jeng Lin; Shi Chiang Liao; Jeng Hsin Huang

doi:10.1016/S0925-2312(00)00351-9

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

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

電機工程學系

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	???core.languages.en_GB???
頁（從 - 到）	197-208
頁數	12
期刊	Neurocomputing
卷	37
發行號	1-4
DOIs	https://doi.org/10.1016/S0925-2312(00)00351-9
出版狀態	已出版 - 2001

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10.1016/S0925-2312(00)00351-9

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abstract = "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.",

keywords = "Competitive learning, Fuzzy clustering, Image coding, Vector quantization",

author = "Hwang, {Wen Jyi} and Lin, {Faa Jeng} and Liao, {Shi Chiang} and Huang, {Jeng Hsin}",

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T1 - A novel fuzzy entropy-constrained competitive learning algorithm for image coding

AU - Hwang, Wen Jyi

AU - Lin, Faa Jeng

AU - Liao, Shi Chiang

AU - Huang, Jeng Hsin

PY - 2001

Y1 - 2001

N2 - 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.

AB - 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.

KW - Competitive learning

KW - Fuzzy clustering

KW - Image coding

KW - Vector quantization

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U2 - 10.1016/S0925-2312(00)00351-9

DO - 10.1016/S0925-2312(00)00351-9

M3 - 期刊論文

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EP - 208

JO - Neurocomputing

JF - Neurocomputing

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ER -

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

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