Genetic fuzzy entropy-constrained vector quantization

Wen Jyi Hwang; Ching Fung Chine; Sheng Lin Hong

doi:10.1080/02533839.2001.9670634

Genetic fuzzy entropy-constrained vector quantization

Wen Jyi Hwang^*, Ching Fung Chine, Sheng Lin Hong

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

A novel variable-rate vector quantizer (VQ) design algorithm using both genetic and fuzzy clustering techniques is presented. The algorithm, termed genetic fuzzy entropy-constrained VQ (GFECVQ) design algorithm, has a superior rate-distortion performance than that of the existing variable-rate VQ design algorithms. The algorithm utilizes fuzzy clustering technique to enhance the rate-distortion performance for the VQ design. In addition, a novel genetic algorithm is employed to ensure the robustness of the performance against the selection of initial parameters. Simulation results demonstrate that the FECVQ can be an effective alternative for the design of variable-rate VQs.

Original language	English
Pages (from-to)	369-377
Number of pages	9
Journal	Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an
Volume	24
Issue number	3
DOIs	https://doi.org/10.1080/02533839.2001.9670634
Publication status	Published - 2001
Externally published	Yes

Keywords

Fuzzy C-means algorithm
Fuzzy clustering
Image compression
Vector quantization

ASJC Scopus subject areas

General Engineering

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10.1080/02533839.2001.9670634

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title = "Genetic fuzzy entropy-constrained vector quantization",

abstract = "A novel variable-rate vector quantizer (VQ) design algorithm using both genetic and fuzzy clustering techniques is presented. The algorithm, termed genetic fuzzy entropy-constrained VQ (GFECVQ) design algorithm, has a superior rate-distortion performance than that of the existing variable-rate VQ design algorithms. The algorithm utilizes fuzzy clustering technique to enhance the rate-distortion performance for the VQ design. In addition, a novel genetic algorithm is employed to ensure the robustness of the performance against the selection of initial parameters. Simulation results demonstrate that the FECVQ can be an effective alternative for the design of variable-rate VQs.",

keywords = "Fuzzy C-means algorithm, Fuzzy clustering, Image compression, Vector quantization",

author = "Hwang, {Wen Jyi} and Chine, {Ching Fung} and Hong, {Sheng Lin}",

year = "2001",

doi = "10.1080/02533839.2001.9670634",

language = "English",

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pages = "369--377",

journal = "Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an",

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publisher = "Taylor and Francis Ltd.",

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TY - JOUR

T1 - Genetic fuzzy entropy-constrained vector quantization

AU - Hwang, Wen Jyi

AU - Chine, Ching Fung

AU - Hong, Sheng Lin

PY - 2001

Y1 - 2001

N2 - A novel variable-rate vector quantizer (VQ) design algorithm using both genetic and fuzzy clustering techniques is presented. The algorithm, termed genetic fuzzy entropy-constrained VQ (GFECVQ) design algorithm, has a superior rate-distortion performance than that of the existing variable-rate VQ design algorithms. The algorithm utilizes fuzzy clustering technique to enhance the rate-distortion performance for the VQ design. In addition, a novel genetic algorithm is employed to ensure the robustness of the performance against the selection of initial parameters. Simulation results demonstrate that the FECVQ can be an effective alternative for the design of variable-rate VQs.

AB - A novel variable-rate vector quantizer (VQ) design algorithm using both genetic and fuzzy clustering techniques is presented. The algorithm, termed genetic fuzzy entropy-constrained VQ (GFECVQ) design algorithm, has a superior rate-distortion performance than that of the existing variable-rate VQ design algorithms. The algorithm utilizes fuzzy clustering technique to enhance the rate-distortion performance for the VQ design. In addition, a novel genetic algorithm is employed to ensure the robustness of the performance against the selection of initial parameters. Simulation results demonstrate that the FECVQ can be an effective alternative for the design of variable-rate VQs.

KW - Fuzzy C-means algorithm

KW - Fuzzy clustering

KW - Image compression

KW - Vector quantization

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JO - Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an

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ER -

Genetic fuzzy entropy-constrained vector quantization

Abstract

Keywords

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