Efficient vector quantization using genetic algorithm

Hongwei Sun; Kwok-Yan Lam; Siu-Leung Chung; Weiming Dong; Ming Gu; Jiaguang Sun

doi:10.1007/s00521-004-0455-7

Efficient vector quantization using genetic algorithm

Hongwei Sun, Kwok-Yan Lam, Siu-Leung Chung^*, Weiming Dong, Ming Gu, Jiaguang Sun

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

24 Citations (Scopus)

Abstract

This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance.

Original language	English
Pages (from-to)	203-211
Journal	Neural Computing and Applications
Volume	14
Issue number	3
Online published	31 May 2005
DOIs	https://doi.org/10.1007/s00521-004-0455-7
Publication status	Published - Sept 2005
Externally published	Yes

Research Keywords

Genetic algorithm
Image compression
Principal component analysis
Vector quantization

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title = "Efficient vector quantization using genetic algorithm",

abstract = "This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance.",

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T1 - Efficient vector quantization using genetic algorithm

AU - Sun, Hongwei

AU - Lam, Kwok-Yan

AU - Chung, Siu-Leung

AU - Dong, Weiming

AU - Gu, Ming

AU - Sun, Jiaguang

PY - 2005/9

Y1 - 2005/9

N2 - This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance.

AB - This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance.

KW - Genetic algorithm

KW - Image compression

KW - Principal component analysis

KW - Vector quantization

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-28844473036&origin=recordpage

U2 - 10.1007/s00521-004-0455-7

DO - 10.1007/s00521-004-0455-7

M3 - RGC 21 - Publication in refereed journal

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VL - 14

SP - 203

EP - 211

JO - Neural Computing and Applications

JF - Neural Computing and Applications

IS - 3

ER -

Efficient vector quantization using genetic algorithm

Abstract

Research Keywords

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