Swarm and Evolutionary Algorithms in Image Compression by F-Transform
This article investigates the application of swarm and evolutionary algorithms, namely the SOMA, DE, and GA, for optimizing the F-transform-based image compression. To do this, we introduce the cost function, evaluating the approximation of decompressed images to the original image, concerning the p...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10061419/ |
| _version_ | 1797866974188929024 |
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| author | Nguyen Le Toan Nhat Linh Quoc Bao Diep |
| author_facet | Nguyen Le Toan Nhat Linh Quoc Bao Diep |
| author_sort | Nguyen Le Toan Nhat Linh |
| collection | DOAJ |
| description | This article investigates the application of swarm and evolutionary algorithms, namely the SOMA, DE, and GA, for optimizing the F-transform-based image compression. To do this, we introduce the cost function, evaluating the approximation of decompressed images to the original image, concerning the parameters that control the approximation quality of the F-transform. This function is then minimized by the selected algorithms to find optimal settings for image compression and decompression. We design experiments to compare the performance of the original F-transform method and the methods optimized by SOMA, DE, and GA on a dataset of 10 pictures. In all considered cases, the results obtained with the optimized method completely surpass those obtained by the original one. We also apply a statistical test (called Wilcoxon signed-rank test) for ranking the performance of selected algorithms in this issue. The results show that the SOMA and DE perform well in cases where the compressed image sizes are small. However, the GA algorithm shows outperformance in comparison with the others in more complicated cases where the compressed image size is bigger. The outperformance of the GA is in terms of decompression quality and computation time. Finally, we provide a visual comparison between the original F-transform-based method and the method optimized by the GA, tested on a <inline-formula> <tex-math notation="LaTeX">$128\times 128$ </tex-math></inline-formula> picture. The decompressed image by the latter is much sharper and more detailed than that obtained by the former. |
| first_indexed | 2024-04-09T23:33:57Z |
| format | Article |
| id | doaj.art-5b75c04abf39439d97398615967d5fac |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T23:33:57Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5b75c04abf39439d97398615967d5fac2023-03-20T23:00:19ZengIEEEIEEE Access2169-35362023-01-0111259912600310.1109/ACCESS.2023.325354310061419Swarm and Evolutionary Algorithms in Image Compression by F-TransformNguyen Le Toan Nhat Linh0https://orcid.org/0000-0003-2200-6983Quoc Bao Diep1https://orcid.org/0000-0003-4050-648XApplied Analysis Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, VietnamFaculty of Mechanical-Electrical and Computer Engineering, School of Technology, Van Lang University, Ho Chi Minh City, VietnamThis article investigates the application of swarm and evolutionary algorithms, namely the SOMA, DE, and GA, for optimizing the F-transform-based image compression. To do this, we introduce the cost function, evaluating the approximation of decompressed images to the original image, concerning the parameters that control the approximation quality of the F-transform. This function is then minimized by the selected algorithms to find optimal settings for image compression and decompression. We design experiments to compare the performance of the original F-transform method and the methods optimized by SOMA, DE, and GA on a dataset of 10 pictures. In all considered cases, the results obtained with the optimized method completely surpass those obtained by the original one. We also apply a statistical test (called Wilcoxon signed-rank test) for ranking the performance of selected algorithms in this issue. The results show that the SOMA and DE perform well in cases where the compressed image sizes are small. However, the GA algorithm shows outperformance in comparison with the others in more complicated cases where the compressed image size is bigger. The outperformance of the GA is in terms of decompression quality and computation time. Finally, we provide a visual comparison between the original F-transform-based method and the method optimized by the GA, tested on a <inline-formula> <tex-math notation="LaTeX">$128\times 128$ </tex-math></inline-formula> picture. The decompressed image by the latter is much sharper and more detailed than that obtained by the former.https://ieeexplore.ieee.org/document/10061419/Image compressionswarm intelligenceevolutionary algorithmsnumerical optimization |
| spellingShingle | Nguyen Le Toan Nhat Linh Quoc Bao Diep Swarm and Evolutionary Algorithms in Image Compression by F-Transform IEEE Access Image compression swarm intelligence evolutionary algorithms numerical optimization |
| title | Swarm and Evolutionary Algorithms in Image Compression by F-Transform |
| title_full | Swarm and Evolutionary Algorithms in Image Compression by F-Transform |
| title_fullStr | Swarm and Evolutionary Algorithms in Image Compression by F-Transform |
| title_full_unstemmed | Swarm and Evolutionary Algorithms in Image Compression by F-Transform |
| title_short | Swarm and Evolutionary Algorithms in Image Compression by F-Transform |
| title_sort | swarm and evolutionary algorithms in image compression by f transform |
| topic | Image compression swarm intelligence evolutionary algorithms numerical optimization |
| url | https://ieeexplore.ieee.org/document/10061419/ |
| work_keys_str_mv | AT nguyenletoannhatlinh swarmandevolutionaryalgorithmsinimagecompressionbyftransform AT quocbaodiep swarmandevolutionaryalgorithmsinimagecompressionbyftransform |