Learned distributed image compression with decoder side information
With the rapid development of digital communication and the widespread use of the Internet of Things, multi-view image compression has attracted increasing attention as a fundamental technology for image data communication. Multi-view image compression aims to improve compression efficiency by lever...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-04-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864824000683 |
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| author | Yankai Yin Zhe Sun Peiying Ruan Ruidong Li Feng Duan |
| author_facet | Yankai Yin Zhe Sun Peiying Ruan Ruidong Li Feng Duan |
| author_sort | Yankai Yin |
| collection | DOAJ |
| description | With the rapid development of digital communication and the widespread use of the Internet of Things, multi-view image compression has attracted increasing attention as a fundamental technology for image data communication. Multi-view image compression aims to improve compression efficiency by leveraging correlations between images. However, the requirement of synchronization and inter-image communication at the encoder side poses significant challenges, especially for constrained devices. In this study, we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during decoding. Our model integrates an encoder network, a quantization module, and a decoder network, to ensure both high compression performance and high-quality image reconstruction. The encoder uses a deep Convolutional Neural Network (CNN) to extract high-level features from the input image, which then pass through the quantization module for further compression before undergoing lossless entropy coding. The decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder side. Specifically, we first introduce a channel-spatial attention module to capture and refine information within individual image feature maps. Second, we employ a semi-coupled convolution module to extract both shared and specific information in images. Finally, a cross-attention module is employed to fuse mutual information extracted from side information. The effectiveness of our model is validated on various datasets, including KITTI Stereo and Cityscapes. The results highlight the superior compression capabilities of our method, surpassing state-of-the-art techniques. |
| format | Article |
| id | doaj-art-42e2ba73ba72442dbc424dd27f42b485 |
| institution | Kabale University |
| issn | 2352-8648 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj-art-42e2ba73ba72442dbc424dd27f42b4852025-08-20T03:49:03ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482025-04-0111234935810.1016/j.dcan.2024.06.001Learned distributed image compression with decoder side informationYankai Yin0Zhe Sun1Peiying Ruan2Ruidong Li3Feng Duan4Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation, Nankai University, Tianjin 300350, China; Institute of Natural Sciences, Kanazawa University, Ishikawa 9201164, JapanFaculty of Health Data Science and Graduate School of Medicine, Juntendo University, Chiba 2790013, JapanNVIDIA AI Technology Center, NVIDIA Japan, Tokyo 1070052, JapanInstitute of Natural Sciences, Kanazawa University, Ishikawa 9201164, Japan; Corresponding authors.Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation, Nankai University, Tianjin 300350, China; Corresponding authors.With the rapid development of digital communication and the widespread use of the Internet of Things, multi-view image compression has attracted increasing attention as a fundamental technology for image data communication. Multi-view image compression aims to improve compression efficiency by leveraging correlations between images. However, the requirement of synchronization and inter-image communication at the encoder side poses significant challenges, especially for constrained devices. In this study, we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during decoding. Our model integrates an encoder network, a quantization module, and a decoder network, to ensure both high compression performance and high-quality image reconstruction. The encoder uses a deep Convolutional Neural Network (CNN) to extract high-level features from the input image, which then pass through the quantization module for further compression before undergoing lossless entropy coding. The decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder side. Specifically, we first introduce a channel-spatial attention module to capture and refine information within individual image feature maps. Second, we employ a semi-coupled convolution module to extract both shared and specific information in images. Finally, a cross-attention module is employed to fuse mutual information extracted from side information. The effectiveness of our model is validated on various datasets, including KITTI Stereo and Cityscapes. The results highlight the superior compression capabilities of our method, surpassing state-of-the-art techniques.http://www.sciencedirect.com/science/article/pii/S2352864824000683Digital communicationImage compressionSide informationChannel-spatial attention moduleCross-attention module |
| spellingShingle | Yankai Yin Zhe Sun Peiying Ruan Ruidong Li Feng Duan Learned distributed image compression with decoder side information Digital Communications and Networks Digital communication Image compression Side information Channel-spatial attention module Cross-attention module |
| title | Learned distributed image compression with decoder side information |
| title_full | Learned distributed image compression with decoder side information |
| title_fullStr | Learned distributed image compression with decoder side information |
| title_full_unstemmed | Learned distributed image compression with decoder side information |
| title_short | Learned distributed image compression with decoder side information |
| title_sort | learned distributed image compression with decoder side information |
| topic | Digital communication Image compression Side information Channel-spatial attention module Cross-attention module |
| url | http://www.sciencedirect.com/science/article/pii/S2352864824000683 |
| work_keys_str_mv | AT yankaiyin learneddistributedimagecompressionwithdecodersideinformation AT zhesun learneddistributedimagecompressionwithdecodersideinformation AT peiyingruan learneddistributedimagecompressionwithdecodersideinformation AT ruidongli learneddistributedimagecompressionwithdecodersideinformation AT fengduan learneddistributedimagecompressionwithdecodersideinformation |