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The Scientific World Journal
Volume 2014, Article ID 494319, 17 pages
http://dx.doi.org/10.1155/2014/494319
Research Article

An Evolved Wavelet Library Based on Genetic Algorithm

1Department of Electronics and Communication Engineering, Anna University, Chennai 600025, India
2Department of Electronics and Communication Engineering, Loyola-ICAM College of Engineering and Technology (LICET), Chennai 600034, India

Received 28 January 2014; Revised 31 July 2014; Accepted 28 August 2014; Published 27 October 2014

Academic Editor: Dimitrios A. Karras

Copyright © 2014 D. Vaithiyanathan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As the size of the images being captured increases, there is a need for a robust algorithm for image compression which satiates the bandwidth limitation of the transmitted channels and preserves the image resolution without considerable loss in the image quality. Many conventional image compression algorithms use wavelet transform which can significantly reduce the number of bits needed to represent a pixel and the process of quantization and thresholding further increases the compression. In this paper the authors evolve two sets of wavelet filter coefficients using genetic algorithm (GA), one for the whole image portion except the edge areas and the other for the portions near the edges in the image (i.e., global and local filters). Images are initially separated into several groups based on their frequency content, edges, and textures and the wavelet filter coefficients are evolved separately for each group. As there is a possibility of the GA settling in local maximum, we introduce a new shuffling operator to prevent the GA from this effect. The GA used to evolve filter coefficients primarily focuses on maximizing the peak signal to noise ratio (PSNR). The evolved filter coefficients by the proposed method outperform the existing methods by a 0.31 dB improvement in the average PSNR and a 0.39 dB improvement in the maximum PSNR.