A Genetic Grey-Based Neural Networks with Wavelet Transform for Search of Optimal Codebook
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概要
- 論文の詳細を見る
The wavelet transform (WT) has recently emerged as a powerful tool fur image compression. In this paper, a new image compression technique combining the genetic algorithm (GA) and grey-based competitive learning network (GCLN) in the wavelet transform domain is proposed. In the GCLN, the grey theory is applied to a two-layer modified competitive learning network in order to generate optimal solution for VQ. In accordance with the degree of similarity measure between training vectors and codeveclors, the grey relational analysis is used to measure the relationship degree among them. The GA is used in an attempt to optimize a specified objective function related to vector quantizer design. The physical processes of competition, selection and reproduction operating in populations are adopted in combination with GCLN to produce a superior genetic grey-based competitive learning network (GGCLN) for codebook design in image compression. The experimental results show that a promising codebook can be obtained using the proposed GGCLN and GGCLN with wavelet decomposition.
- 社団法人電子情報通信学会の論文
- 2003-03-01
著者
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Lin C‐y
Department Of Electrical Engineering National Cheng Kung University:department Of Electronic Enginee
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CHEN Chin-Hsing
Department of Management Information Systems, Central Taiwan University of Sciences and Technology
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Lin Chi-yuan
Department Of Electrical Engineering National Cheng Kung University:department Of Electronic Enginee
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Chen Chin-hsing
Department Of Electrical Engineering National Cheng Kung University
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