High dynamic range (HDR) video offers a more realistic visual experience than standard dynamic range (SDR) video, while introducing new challenges to both compression and transmission. Rate control is an effective technology to overcome these challenges, and ensure optimal HDR video delivery. However, the rate control algorithm in the latest video coding standard, versatile video coding (VVC), is tailored to SDR videos, and does not produce well coding results when encoding HDR videos. To address this problem, a data-driven $\lambda $ -domain rate control algorithm is proposed for VVC HDR intra frames in this paper. First, the coding characteristics of HDR intra coding are analyzed, and a piecewise R- $\lambda $ model is proposed to accurately determine the correlation between the rate (R) and the Lagrange parameter $\lambda $ for HDR intra frames. Then, to optimize bit allocation at the coding tree unit (CTU)-level, a wavelet-based residual neural network (WRNN) is developed to accurately predict the parameters of the piecewise R- $\lambda $ model for each CTU. Third, a large-scale HDR dataset is established for training WRNN, which facilitates the applications of deep learning in HDR intra coding. Extensive experimental results show that our proposed HDR intra frame rate control algorithm achieves superior coding results than the state-of-the-art algorithms. The source code of this work will be released at https://github.com/TJU-Videocoding/WRNN.git .

中文翻译：

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通过基于小波的残差神经网络对 VVC HDR 内部编码进行 λ 域速率控制


与标准动态范围 （SDR） 视频相比，高动态范围 （HDR） 视频提供更逼真的视觉体验，同时也给压缩和传输带来了新的挑战。速率控制是克服这些挑战并确保最佳 HDR 视频交付的有效技术。但是，最新视频编码标准 VVC 中的速率控制算法是针对 SDR 视频量身定制的，在编码 HDR 视频时无法产生良好的编码结果。针对这一问题，该文针对VVC HDR帧内帧提出了一种数据驱动的$\lambda $-domain速率控制算法。首先，分析了HDR帧内编码的编码特性，并提出了一种分段R-$-lambda $模型来准确判断HDR帧内帧的速率（R）与拉格朗日参数$-lambda $之间的相关性。然后，为了在编码树单元 （CTU） 级别优化比特分配，开发了一种基于小波的残差神经网络 （WRNN），以准确预测每个 CTU 的分段 R- $\lambda $ 模型的参数。再次，建立大规模HDR数据集用于训练WRNN，有利于深度学习在HDR内部编码中的应用。大量的实验结果表明，我们提出的 HDR 帧内控制算法取得了优于最先进的算法的编码结果。本作品的源代码将于 https://github.com/TJU-Videocoding/WRNN.git 发布。