Effective image compression using transformer and residual network for balanced handling of high and low-frequency information
Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. However, the low-frequency information in the image can...
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| Published in: | PloS one Vol. 20; no. 10; p. e0333376 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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03.10.2025
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. However, the low-frequency information in the image cannot be obtained well through the Transformer network. To address this issue, the paper introduces a novel end-to-end autoencoder architecture for image compression based on the transformer and residual network. This method, called Transformer and Residual Network (TRN), offers a comprehensive solution for efficient image compression, capturing essential image content while effectively reducing data size. The TRN employs a dual network, comprising a self-attention pathway and a residual network, intricately designed as a high-low-frequency mixer. This dual-network can preserve both high and low-frequency features during image compression. The end-to-end training of this model employs rate-distortion optimization (RDO methods). Experimental results demonstrate that the proposed TRN method outperforms the latest deep learning-based image compression methods, achieving an impressive 8.32% BD-rate (bit-rate distortion performance) improvement on the CLIC dataset. In comparison to traditional methods like JPEG, the proposed achieves a remarkable BD-rate improvement of 70.35% on the CLIC dataset. |
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| AbstractList | Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. However, the low-frequency information in the image cannot be obtained well through the Transformer network. To address this issue, the paper introduces a novel end-to-end autoencoder architecture for image compression based on the transformer and residual network. This method, called Transformer and Residual Network (TRN), offers a comprehensive solution for efficient image compression, capturing essential image content while effectively reducing data size. The TRN employs a dual network, comprising a self-attention pathway and a residual network, intricately designed as a high-low-frequency mixer. This dual-network can preserve both high and low-frequency features during image compression. The end-to-end training of this model employs rate-distortion optimization (RDO methods). Experimental results demonstrate that the proposed TRN method outperforms the latest deep learning-based image compression methods, achieving an impressive 8.32% BD-rate (bit-rate distortion performance) improvement on the CLIC dataset. In comparison to traditional methods like JPEG, the proposed achieves a remarkable BD-rate improvement of 70.35% on the CLIC dataset. Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. However, the low-frequency information in the image cannot be obtained well through the Transformer network. To address this issue, the paper introduces a novel end-to-end autoencoder architecture for image compression based on the transformer and residual network. This method, called Transformer and Residual Network (TRN), offers a comprehensive solution for efficient image compression, capturing essential image content while effectively reducing data size. The TRN employs a dual network, comprising a self-attention pathway and a residual network, intricately designed as a high-low-frequency mixer. This dual-network can preserve both high and low-frequency features during image compression. The end-to-end training of this model employs rate-distortion optimization (RDO methods). Experimental results demonstrate that the proposed TRN method outperforms the latest deep learning-based image compression methods, achieving an impressive 8.32% BD-rate (bit-rate distortion performance) improvement on the CLIC dataset. In comparison to traditional methods like JPEG, the proposed achieves a remarkable BD-rate improvement of 70.35% on the CLIC dataset.Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. However, the low-frequency information in the image cannot be obtained well through the Transformer network. To address this issue, the paper introduces a novel end-to-end autoencoder architecture for image compression based on the transformer and residual network. This method, called Transformer and Residual Network (TRN), offers a comprehensive solution for efficient image compression, capturing essential image content while effectively reducing data size. The TRN employs a dual network, comprising a self-attention pathway and a residual network, intricately designed as a high-low-frequency mixer. This dual-network can preserve both high and low-frequency features during image compression. The end-to-end training of this model employs rate-distortion optimization (RDO methods). Experimental results demonstrate that the proposed TRN method outperforms the latest deep learning-based image compression methods, achieving an impressive 8.32% BD-rate (bit-rate distortion performance) improvement on the CLIC dataset. In comparison to traditional methods like JPEG, the proposed achieves a remarkable BD-rate improvement of 70.35% on the CLIC dataset. |
| Audience | Academic |
| Author | Nie, Wei Luo, Guixiang Feng, Xiangfei Yuan, Zhanjiang Hu, Jianhua Yang, Jiahui |
| Author_xml | – sequence: 1 givenname: Jianhua surname: Hu fullname: Hu, Jianhua – sequence: 2 givenname: Guixiang orcidid: 0009-0008-9979-3069 surname: Luo fullname: Luo, Guixiang – sequence: 3 givenname: Xiangfei surname: Feng fullname: Feng, Xiangfei – sequence: 4 givenname: Zhanjiang surname: Yuan fullname: Yuan, Zhanjiang – sequence: 5 givenname: Jiahui surname: Yang fullname: Yang, Jiahui – sequence: 6 givenname: Wei orcidid: 0000-0003-0060-9863 surname: Nie fullname: Nie, Wei |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41042778$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/TPAMI.2023.3322904 10.1109/TPAMI.2025.3560090 10.1109/ICCV48922.2021.00986 10.1109/TCSVT.2025.3548308 10.1109/VCIP63160.2024.10849935 10.1109/CVPR.2017.577 10.1109/CVPR46437.2021.01270 10.1109/CVPRW.2017.150 10.1109/CVPR52729.2023.01383 10.1109/CVPR42600.2020.00796 10.1109/DCC58796.2024.00077 10.1016/j.optlastec.2025.112616 |
| ContentType | Journal Article |
| Copyright | Copyright: © 2025 Hu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2025 Public Library of Science 2025 Hu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2025 Hu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Algorithms Analysis Compression Data compression Data Compression - methods Data reduction Datasets Deep Learning Design Distortion Entropy Humans Image compression Image processing Image Processing, Computer-Assisted - methods Methods Neural networks Neural Networks, Computer |
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| Title | Effective image compression using transformer and residual network for balanced handling of high and low-frequency information |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/41042778 https://www.proquest.com/docview/3257043599 https://www.proquest.com/docview/3257102184 https://doaj.org/article/caff91ccb413430298cfa08fb7fb1b4f http://dx.doi.org/10.1371/journal.pone.0333376 |
| Volume | 20 |
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