Genetic entropy-constrained vector quantizer design algorithm

Wen Jyi Hwang; Sheng Lin Hong

doi:10.1117/1.602081

Genetic entropy-constrained vector quantizer design algorithm

Wen Jyi Hwang^*, Sheng Lin Hong

^*Corresponding author for this work

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

12 Citations (Scopus)

Abstract

A novel variable-rate vector quantizer (VQ) design algorithm, which is a hybrid approach combining a genetic algorithm with the entropy-constrained VQ (ECVQ) algorithm is presented. The proposed technique outperforms the ECVQ algorithm in the sense that it reaches a nearby global optimum rather than a local one. Simulation results show that, when applied to image coding, the technique achieves higher peak SNR (PSNR) and image quality than techniques using the ECVQ algorithm.

Original language	English
Pages (from-to)	233-239
Number of pages	7
Journal	Optical Engineering
Volume	38
Issue number	2
DOIs	https://doi.org/10.1117/1.602081
Publication status	Published - 1999 Feb
Externally published	Yes

Keywords

Genetic algorithm
Global optimization
Image compression
Vector quantization

ASJC Scopus subject areas

Atomic and Molecular Physics, and Optics
General Engineering

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10.1117/1.602081

Cite this

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abstract = "A novel variable-rate vector quantizer (VQ) design algorithm, which is a hybrid approach combining a genetic algorithm with the entropy-constrained VQ (ECVQ) algorithm is presented. The proposed technique outperforms the ECVQ algorithm in the sense that it reaches a nearby global optimum rather than a local one. Simulation results show that, when applied to image coding, the technique achieves higher peak SNR (PSNR) and image quality than techniques using the ECVQ algorithm.",

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

year = "1999",

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AU - Hong, Sheng Lin

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AB - A novel variable-rate vector quantizer (VQ) design algorithm, which is a hybrid approach combining a genetic algorithm with the entropy-constrained VQ (ECVQ) algorithm is presented. The proposed technique outperforms the ECVQ algorithm in the sense that it reaches a nearby global optimum rather than a local one. Simulation results show that, when applied to image coding, the technique achieves higher peak SNR (PSNR) and image quality than techniques using the ECVQ algorithm.

KW - Genetic algorithm

KW - Global optimization

KW - Image compression

KW - Vector quantization

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Genetic entropy-constrained vector quantizer design algorithm

Abstract

Keywords

ASJC Scopus subject areas

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