Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Framework
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911261289086976 |
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| author | Zhang, Zifu Li, Shengxi Sun, Xiancheng Xu, Mai Liu, Zhengyuan Xia, Jingyuan |
| author_facet | Zhang, Zifu Li, Shengxi Sun, Xiancheng Xu, Mai Liu, Zhengyuan Xia, Jingyuan |
| contents | Human-machine collaborative compression has been receiving increasing research efforts for reducing image/video data, serving as the basis for both human perception and machine intelligence. Existing collaborative methods are dominantly built upon the de facto human-vision compression pipeline, witnessing deficiency on complexity and bit-rates when aggregating the machine-vision compression. Indeed, machine vision solely focuses on the core regions within the image/video, requiring much less information compared with the compressed information for human vision. In this paper, we thus set out the first successful attempt by a novel collaborative compression method based on the machine-vision-oriented compression, instead of human-vision pipeline. In other words, machine vision serves as the basis for human vision within collaborative compression. A plug-and-play variable bit-rate strategy is also developed for machine vision tasks. Then, we propose to progressively aggregate the semantics from the machine-vision compression, whilst seamlessly tailing the diffusion prior to restore high-fidelity details for human vision, thus named as diffusion-prior based feature compression for human and machine visions (Diff-FCHM). Experimental results verify the consistently superior performances of our Diff-FCHM, on both machine-vision and human-vision compression with remarkable margins. Our code will be released upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Framework Zhang, Zifu Li, Shengxi Sun, Xiancheng Xu, Mai Liu, Zhengyuan Xia, Jingyuan Computer Vision and Pattern Recognition Human-machine collaborative compression has been receiving increasing research efforts for reducing image/video data, serving as the basis for both human perception and machine intelligence. Existing collaborative methods are dominantly built upon the de facto human-vision compression pipeline, witnessing deficiency on complexity and bit-rates when aggregating the machine-vision compression. Indeed, machine vision solely focuses on the core regions within the image/video, requiring much less information compared with the compressed information for human vision. In this paper, we thus set out the first successful attempt by a novel collaborative compression method based on the machine-vision-oriented compression, instead of human-vision pipeline. In other words, machine vision serves as the basis for human vision within collaborative compression. A plug-and-play variable bit-rate strategy is also developed for machine vision tasks. Then, we propose to progressively aggregate the semantics from the machine-vision compression, whilst seamlessly tailing the diffusion prior to restore high-fidelity details for human vision, thus named as diffusion-prior based feature compression for human and machine visions (Diff-FCHM). Experimental results verify the consistently superior performances of our Diff-FCHM, on both machine-vision and human-vision compression with remarkable margins. Our code will be released upon acceptance. |
| title | Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Framework |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.08915 |