Application of Multimedia Semantic Extraction Method in Fast Image Enhancement Control
In order to solve the problem that it is difficult to effectively enhance the details of the compressed domain and maintain the overall brightness and clarity of the image when improving the image contrast in the current image enhancement method in the compressed domain, a multimedia semantic extrac...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/2282217 |
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| _version_ | 1832549252244439040 |
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| author | Yuyang Gao Ming Chen Shouqing Du Guofu Feng |
| author_facet | Yuyang Gao Ming Chen Shouqing Du Guofu Feng |
| author_sort | Yuyang Gao |
| collection | DOAJ |
| description | In order to solve the problem that it is difficult to effectively enhance the details of the compressed domain and maintain the overall brightness and clarity of the image when improving the image contrast in the current image enhancement method in the compressed domain, a multimedia semantic extraction method is applied in fast image enhancement control. It has been proposed that thealgorithm that synthesizes training samples according to the Retinex model converts the original low-light image from RGB (red-green-blue) space to HSI (hue saturation intensity) color space, keeps the chrominance and saturation components unchanged, and uses DCNN to enhance the luminance component; finally, it converts the HSI color space to RGB space to get the final enhanced image. The experimental results show that the performance of the model will increase with the increase of the number of convolution kernels, but the increase of the number of convolution kernels will undoubtedly increase the amount of calculation; it can also be found that when the number of network layers is 7, the PSNR of the image output by the model increases. The highest value, increasing the number of network layers, does not necessarily improve the performance of the model; with or without BN, his training method converges more easily than direct RGB image enhancement, with higher average PSNR and SSIM values. The experimental results show that, compared with the traditional Retinex enhancement algorithm and the DCT compression domain enhancement algorithm, the algorithm has better detail enhancement and color preservation effects and can better suppress the block effect. |
| format | Article |
| id | doaj-art-abfb6d63d338483c90ea2c07eaf140fc |
| institution | Kabale University |
| issn | 1687-5257 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-abfb6d63d338483c90ea2c07eaf140fc2025-02-03T06:11:53ZengWileyJournal of Control Science and Engineering1687-52572022-01-01202210.1155/2022/2282217Application of Multimedia Semantic Extraction Method in Fast Image Enhancement ControlYuyang Gao0Ming Chen1Shouqing Du2Guofu Feng3College of InformationCollege of InformationCollege of InformationCollege of InformationIn order to solve the problem that it is difficult to effectively enhance the details of the compressed domain and maintain the overall brightness and clarity of the image when improving the image contrast in the current image enhancement method in the compressed domain, a multimedia semantic extraction method is applied in fast image enhancement control. It has been proposed that thealgorithm that synthesizes training samples according to the Retinex model converts the original low-light image from RGB (red-green-blue) space to HSI (hue saturation intensity) color space, keeps the chrominance and saturation components unchanged, and uses DCNN to enhance the luminance component; finally, it converts the HSI color space to RGB space to get the final enhanced image. The experimental results show that the performance of the model will increase with the increase of the number of convolution kernels, but the increase of the number of convolution kernels will undoubtedly increase the amount of calculation; it can also be found that when the number of network layers is 7, the PSNR of the image output by the model increases. The highest value, increasing the number of network layers, does not necessarily improve the performance of the model; with or without BN, his training method converges more easily than direct RGB image enhancement, with higher average PSNR and SSIM values. The experimental results show that, compared with the traditional Retinex enhancement algorithm and the DCT compression domain enhancement algorithm, the algorithm has better detail enhancement and color preservation effects and can better suppress the block effect.http://dx.doi.org/10.1155/2022/2282217 |
| spellingShingle | Yuyang Gao Ming Chen Shouqing Du Guofu Feng Application of Multimedia Semantic Extraction Method in Fast Image Enhancement Control Journal of Control Science and Engineering |
| title | Application of Multimedia Semantic Extraction Method in Fast Image Enhancement Control |
| title_full | Application of Multimedia Semantic Extraction Method in Fast Image Enhancement Control |
| title_fullStr | Application of Multimedia Semantic Extraction Method in Fast Image Enhancement Control |
| title_full_unstemmed | Application of Multimedia Semantic Extraction Method in Fast Image Enhancement Control |
| title_short | Application of Multimedia Semantic Extraction Method in Fast Image Enhancement Control |
| title_sort | application of multimedia semantic extraction method in fast image enhancement control |
| url | http://dx.doi.org/10.1155/2022/2282217 |
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