无需训练即可动态调整图像编码比特率，提升机器视觉效率。
Training-Free Adaptive Quantization for Variable Rate Image Coding for Machines
- 利用超先验网络的尺度参数，自适应调节量化步长。
- 实现连续比特率控制，最高节省11.07%的BD-rate。
- 适合需要灵活压缩的机器视觉部署场景。
随着计算机视觉技术在现实应用中的快速融合，面向机器的图像编码（ICM）变得日益重要。然而，大多数基于神经网络的ICM框架仅支持固定比特率，需为每个目标比特率单独训练，限制了实际应用。现有可变比特率方法虽缓解此问题，但通常依赖额外训练，增加计算开销并复杂化部署。此外，可变比特率控制在ICM中尚未充分探索。为此，我们提出一种无需训练的量化强度调控框架，实现灵活比特率调整。通过利用超先验网络预测的尺度参数，该方法在通道和空间维度上自适应调节量化步长，使模型保留语义重要区域，同时对非关键区域进行粗量化。架构设计支持通过单一参数实现连续比特率控制。实验表明，所提方法相比非自适应可变比特率基线，最高实现11.07%的BD-rate节省。代码已公开于https://github.com/qwert-top/AQVR-ICM。
原文摘要 · Abstract (English)
Image Coding for Machines (ICM) has become increasingly important with the rapid integration of computer vision technology into real-world applications. However, most neural network-based ICM frameworks operate at a fixed rate, thus requiring individual training for each target bitrate. This limitation may restrict their practical usage. Existing variable rate image compression approaches mitigate this issue but often rely on additional training, which increases computational costs and complicates deployment. Moreover, variable rate control has not been thoroughly explored for ICM. To address these challenges, we propose a training-free framework for quantization strength control which enables flexible bitrate adjustment. By exploiting the scale parameter predicted by the hyperprior network, the proposed method adaptively modulates quantization step sizes across both channel and spatial dimensions. This allows the model to preserve semantically important regions while coarsely quantizing less critical areas. Our architectural design further enables continuous bitrate control through a single parameter. Experimental results demonstrate the effectiveness of our proposed method, achieving up to 11.07% BD-rate savings over the non-adaptive variable rate baseline. The code is available at https://github.com/qwert-top/AQVR-ICM.
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