Efficient vector quantization using genetic algorithm

Authors:
Hongwei Sun;Kwok-Yan Lam;Siu-Leung Chung;Weiming Dong;Ming Gu;Jiaguang Sun
Affiliations:
School of Software, Tsinghua University, Beijing, People’s Republic of China;School of Software, Tsinghua University, Beijing, People’s Republic of China;School of Business and Administration, The Open University of Hong Kong, Kowloon, Hong Kong;School of Software, Tsinghua University, Beijing, People’s Republic of China;School of Software, Tsinghua University, Beijing, People’s Republic of China;School of Software, Tsinghua University, Beijing, People’s Republic of China
Venue:
Neural Computing and Applications
Year:
2005

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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.