Enhancement of images compression using channel attention and post-filtering based on deep autoencoder
Image compression is a crucial research topic in today's information age, especially to meet the demand for balanced data compression efficiency with the quality of the resulting image reconstruction. Common methods used for image compression nowadays are based on autoencoders with deep learnin...
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| Format: | Article |
| Language: | English |
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Universitas Ahmad Dahlan
2024-08-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| Subjects: | |
| Online Access: | https://ijain.org/index.php/IJAIN/article/view/1499 |
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| _version_ | 1846099793970987008 |
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| author | Andri Agustav Wirabudi Nurwan Reza Fachrurrozi Pietra Dorand Muhamad Royhan |
| author_facet | Andri Agustav Wirabudi Nurwan Reza Fachrurrozi Pietra Dorand Muhamad Royhan |
| author_sort | Andri Agustav Wirabudi |
| collection | DOAJ |
| description | Image compression is a crucial research topic in today's information age, especially to meet the demand for balanced data compression efficiency with the quality of the resulting image reconstruction. Common methods used for image compression nowadays are based on autoencoders with deep learning foundations. However, these methods have limitations as they only consider residual values in processed images to achieve existing compression efficiency with less satisfying reconstruction results. To address this issue, we introduce the Attention Block mechanism to improve coding efficiency even further. Additionally, we introduce post-filtering methods to enhance the final reconstruction results of images. Experimental results using two datasets, CLIC for training and KODAK for testing, demonstrate that this method outperforms several previous research methods. With an efficiency coding improvement of -28.16%, an average PSNR improvement of 34%, and an MS-SSIM improvement of 8%, the model in this study significantly enhances the rate-distortion (RD) performance compared to previous approaches. |
| format | Article |
| id | doaj-art-4b75c9401af74a9dbc348da36c5700d6 |
| institution | Kabale University |
| issn | 2442-6571 2548-3161 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj-art-4b75c9401af74a9dbc348da36c5700d62024-12-31T05:50:57ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612024-08-0110342544010.26555/ijain.v10i3.1499306Enhancement of images compression using channel attention and post-filtering based on deep autoencoderAndri Agustav Wirabudi0Nurwan Reza Fachrurrozi1Pietra Dorand2Muhamad Royhan3Department of Intelligence Media Engineering, Hanbat National University School of Applied ScienceSchool of Applied Science, Telkom UniversitySchool of Applied Science, Telkom UniversitySchool of Applied Science, Telkom UniversityImage compression is a crucial research topic in today's information age, especially to meet the demand for balanced data compression efficiency with the quality of the resulting image reconstruction. Common methods used for image compression nowadays are based on autoencoders with deep learning foundations. However, these methods have limitations as they only consider residual values in processed images to achieve existing compression efficiency with less satisfying reconstruction results. To address this issue, we introduce the Attention Block mechanism to improve coding efficiency even further. Additionally, we introduce post-filtering methods to enhance the final reconstruction results of images. Experimental results using two datasets, CLIC for training and KODAK for testing, demonstrate that this method outperforms several previous research methods. With an efficiency coding improvement of -28.16%, an average PSNR improvement of 34%, and an MS-SSIM improvement of 8%, the model in this study significantly enhances the rate-distortion (RD) performance compared to previous approaches.https://ijain.org/index.php/IJAIN/article/view/1499channel attentionauto encoderpost-filteringdeep learningautoendcoder |
| spellingShingle | Andri Agustav Wirabudi Nurwan Reza Fachrurrozi Pietra Dorand Muhamad Royhan Enhancement of images compression using channel attention and post-filtering based on deep autoencoder IJAIN (International Journal of Advances in Intelligent Informatics) channel attention auto encoder post-filtering deep learning autoendcoder |
| title | Enhancement of images compression using channel attention and post-filtering based on deep autoencoder |
| title_full | Enhancement of images compression using channel attention and post-filtering based on deep autoencoder |
| title_fullStr | Enhancement of images compression using channel attention and post-filtering based on deep autoencoder |
| title_full_unstemmed | Enhancement of images compression using channel attention and post-filtering based on deep autoencoder |
| title_short | Enhancement of images compression using channel attention and post-filtering based on deep autoencoder |
| title_sort | enhancement of images compression using channel attention and post filtering based on deep autoencoder |
| topic | channel attention auto encoder post-filtering deep learning autoendcoder |
| url | https://ijain.org/index.php/IJAIN/article/view/1499 |
| work_keys_str_mv | AT andriagustavwirabudi enhancementofimagescompressionusingchannelattentionandpostfilteringbasedondeepautoencoder AT nurwanrezafachrurrozi enhancementofimagescompressionusingchannelattentionandpostfilteringbasedondeepautoencoder AT pietradorand enhancementofimagescompressionusingchannelattentionandpostfilteringbasedondeepautoencoder AT muhamadroyhan enhancementofimagescompressionusingchannelattentionandpostfilteringbasedondeepautoencoder |