用神经网络建模视频编码的码率-质量关系，实现动态自适应。
A Rate-Quality Model for Learned Video Coding
- 构建R-QNet神经网络，根据内容和编码上下文预测码率与质量关系。
- 通过最小二乘法融合前后帧数据，实时优化模型参数，误差更小。
- 适合需要低延迟、高精度码率控制的在线视频编码场景。
学习型视频编码（LVC）近期取得了优异的编码性能。本文提出一种参数化函数来建模学习型视频编码的码率-质量（R-Q）关系。我们训练一个名为RQNet的神经网络，根据视频内容和编码上下文表征码率与质量水平之间的关系。预测的(R,Q)结果通过最小二乘法与先前已编码帧的数据结合，实时确定R-Q模型的参数。相比传统方法，本方法能更准确地估计R-Q关系，支持在线参数自适应，显著提升灵活性与精度。实验表明，在常用数据集上，该模型的码率偏差显著小于基线方法，且额外计算开销极小。
原文摘要 · Abstract (English)
Learned video coding (LVC) has recently achieved superior coding performance. In this paper, we model the rate-quality (R-Q) relationship for learned video coding by a parametric function. We learn a neural network, termed RQNet, to characterize the relationship between the bitrate and quality level according to video content and coding context. The predicted (R,Q) results are further integrated with those from previously coded frames using the least-squares method to determine the parameters of our R-Q model on-the-fly. Compared to the conventional approaches, our method accurately estimates the R-Q relationship, enabling the online adaptation of model parameters to enhance both flexibility and precision. Experimental results show that our R-Q model achieves significantly smaller bitrate deviations than the baseline method on commonly used datasets with minimal additional complexity.
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