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

Department of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	197-208
Number of pages	12
Journal	Neurocomputing
Volume	37
Issue number	1-4
DOIs	https://doi.org/10.1016/S0925-2312(00)00351-9
State	Published - 2001

Keywords

Competitive learning
Fuzzy clustering
Image coding
Vector quantization

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10.1016/S0925-2312(00)00351-9

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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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SN - 0925-2312

VL - 37

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EP - 208

JO - Neurocomputing

JF - Neurocomputing

IS - 1-4

ER -

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

Abstract

Keywords

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