A Genetic Grey-Based Neural Networks with Wavelet Transform for Search of Optimal Codebook

Chi-Yuan LIN; Chin-Hsing CHEN

A Genetic Grey-Based Neural Networks with Wavelet Transform for Search of Optimal Codebook

Chi-Yuan LIN, Chin-Hsing CHEN

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The wavelet transform (WT) has recently emerged as a powerful tool for 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 codevectors, 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.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.3 pp.715-721

Publication Date: 2003/03/01

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Type of Manuscript: PAPER

Category: Neural Networks and Bioengineering

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Chi-Yuan LIN, Chin-Hsing CHEN, "A Genetic Grey-Based Neural Networks with Wavelet Transform for Search of Optimal Codebook" in IEICE TRANSACTIONS on Fundamentals, vol. E86-A, no. 3, pp. 715-721, March 2003, doi: .
Abstract: The wavelet transform (WT) has recently emerged as a powerful tool for 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 codevectors, 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_3_715/_p

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@ARTICLE{e86-a_3_715,
author={Chi-Yuan LIN, Chin-Hsing CHEN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Genetic Grey-Based Neural Networks with Wavelet Transform for Search of Optimal Codebook},
year={2003},
volume={E86-A},
number={3},
pages={715-721},
abstract={The wavelet transform (WT) has recently emerged as a powerful tool for 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 codevectors, 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.},
keywords={},
doi={},
ISSN={},
month={March},}

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TY - JOUR
TI - A Genetic Grey-Based Neural Networks with Wavelet Transform for Search of Optimal Codebook
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 715
EP - 721
AU - Chi-Yuan LIN
AU - Chin-Hsing CHEN
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2003
AB - The wavelet transform (WT) has recently emerged as a powerful tool for 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 codevectors, 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.
ER -