An Evolved Wavelet Library Based on Genetic Algorithm
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...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/494319 |
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| author | D. Vaithiyanathan R. Seshasayanan K. Kunaraj J. Keerthiga |
| author_facet | D. Vaithiyanathan R. Seshasayanan K. Kunaraj J. Keerthiga |
| author_sort | D. Vaithiyanathan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-84bf610b14e64b6dad2044b141ee705b |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-84bf610b14e64b6dad2044b141ee705b2025-02-03T06:12:28ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/494319494319An Evolved Wavelet Library Based on Genetic AlgorithmD. Vaithiyanathan0R. Seshasayanan1K. Kunaraj2J. Keerthiga3Department of Electronics and Communication Engineering, Anna University, Chennai 600025, IndiaDepartment of Electronics and Communication Engineering, Anna University, Chennai 600025, IndiaDepartment of Electronics and Communication Engineering, Loyola-ICAM College of Engineering and Technology (LICET), Chennai 600034, IndiaDepartment of Electronics and Communication Engineering, Anna University, Chennai 600025, IndiaAs 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.http://dx.doi.org/10.1155/2014/494319 |
| spellingShingle | D. Vaithiyanathan R. Seshasayanan K. Kunaraj J. Keerthiga An Evolved Wavelet Library Based on Genetic Algorithm The Scientific World Journal |
| title | An Evolved Wavelet Library Based on Genetic Algorithm |
| title_full | An Evolved Wavelet Library Based on Genetic Algorithm |
| title_fullStr | An Evolved Wavelet Library Based on Genetic Algorithm |
| title_full_unstemmed | An Evolved Wavelet Library Based on Genetic Algorithm |
| title_short | An Evolved Wavelet Library Based on Genetic Algorithm |
| title_sort | evolved wavelet library based on genetic algorithm |
| url | http://dx.doi.org/10.1155/2014/494319 |
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