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Training-Free Continuous Bitrate Control for Scalable Image Coding for Humans and Machines

arXiv CS Tuesday 02 June 2026, 04:00 UTC By Yui Tatsumi, Hiroshi Watanabe 1 min read
Key Points

Announce Type: cross Abstract: Continuous variable-rate compression is highly demanded in real-world applications, but remains underexplored in scalable image coding for humans and machines. In this paper, we propose a training-free variable-rate scalable image coding framework. By adjusting quantization steps based on predicted scale values, the proposed method achieves continuous bitrate control while preserving high-scale information in the machine and enhancement layers.

arXiv:2606.00158v1 Announce Type: cross Abstract: Continuous variable-rate compression is highly demanded in real-world applications, but remains underexplored in scalable image coding for humans and machines. In this paper, we propose a training-free variable-rate scalable image coding framework. By adjusting quantization steps based on predicted scale values, the proposed method achieves continuous bitrate control while preserving high-scale information in the machine and enhancement layers. Experimental results demonstrate the effectiveness of the proposed method and highlight the importance of bitrate allocation between the two layers.
Originally published by arXiv CS Read original →

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