Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Framework

• URL: http://arxiv.org/abs/2511.08915v1
• Date: Thu, 13 Nov 2025 01:17:26 GMT
• Title: Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Framework
• Authors: Zifu Zhang, Shengxi Li, Xiancheng Sun, Mai Xu, Zhengyuan Liu, Jingyuan Xia,
• Abstract summary: We set out the first successful attempt by a novel collaborative compression method based on the machine-vision-oriented compression.<n>A plug-and-play variable bit-rate strategy is also developed for machine vision tasks.<n>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.
• Score: 54.49297832630979
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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