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Efficient vector quantization using genetic algorithm
Sun, Hongwei; Lam, Kwok-Yan; Chung, Siu-Leung; Dong, Weiming; Gu, Ming; Sun, Jia-Guang
Source PublicationNeural Computing & Applications
2005-05
Volume14Issue:3Pages:203-211
AbstractThis 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.
KeywordGenetic Algorithm Principal Component Analysis Vector Quantization Image Compression
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13121
Collection09年以前成果
Corresponding AuthorChung, Siu-Leung
Affiliation1.Tsinghua University
2.The Open University of Hong Kong
Recommended Citation
GB/T 7714
Sun, Hongwei,Lam, Kwok-Yan,Chung, Siu-Leung,et al. Efficient vector quantization using genetic algorithm[J]. Neural Computing & Applications,2005,14(3):203-211.
APA Sun, Hongwei,Lam, Kwok-Yan,Chung, Siu-Leung,Dong, Weiming,Gu, Ming,&Sun, Jia-Guang.(2005).Efficient vector quantization using genetic algorithm.Neural Computing & Applications,14(3),203-211.
MLA Sun, Hongwei,et al."Efficient vector quantization using genetic algorithm".Neural Computing & Applications 14.3(2005):203-211.
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