Training-Free Continuous Bitrate Control for Scalable Image Coding for Humans and Machines
This work addresses the need for continuous bitrate control in scalable image coding for real-world applications involving both human and machine vision.
The paper proposes a training-free variable-rate scalable image coding framework that adjusts quantization steps based on predicted scale values to achieve continuous bitrate control, preserving high-scale information for both humans and machines. Experiments show effectiveness and highlight bitrate allocation importance.
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.