无需训练即可实现人机共用图像编码的连续码率控制。
Training-Free Continuous Bitrate Control for Scalable Image Coding for Humans and Machines
- 通过预测尺度值自适应调整量化步长，实现码率动态调节。
- 人用层与机器用层可独立控制码率，且保持各层关键信息。
- 无需额外训练，适合对效率要求高的实际部署场景。
连续可变码率压缩在真实应用中需求迫切，但在面向人类和机器的可伸缩图像编码中仍研究不足。本文提出一种无需训练的可变码率可伸缩图像编码框架。通过基于预测尺度值自适应调整量化步长，该方法实现了机器层和增强层的独立、连续码率控制，同时保留了各层的重要潜在信息。实验结果验证了该方法的有效性，并凸显了两层间码率分配的重要性。
原文摘要 · Abstract (English)
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 adaptively adjusting quantization step sizes based on predicted scale values, the proposed method enables independent and continuous bitrate control for the machine and enhancement layers while preserving important latent information in each layer. Experimental results demonstrate the effectiveness of the proposed method and highlight the importance of bitrate allocation between the two layers.
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