Deep learning-based in-loop filters have recently demonstrated great improvement for both coding efficiency and subjective quality in video coding. However, most existing deep learning-based in-loop filters tend to develop a sophisticated model in exchange for good performance, and they employ a single network structure to all reconstructed samples, which lack sufficient adaptiveness to the various video content, limiting their performances to some extent. In contrast, this paper proposes an adaptive deep reinforcement learning-based in-loop filter (ARLF) for versatile video coding (VVC). Specifically, we treat the filtering as a decision-making process and employ an agent to select an appropriate network by leveraging recent advances in deep reinforcement learning. To this end, we develop a lightweight backbone and utilize it to design a network set S\mathcal {S} containing networks with different complexities. Then a simple but efficient agent network is designed to predict the optimal network from S\mathcal {S} , which makes the model adaptive to various video contents. To improve the robustness of our model, a two-stage training scheme is further proposed to train the agent and tune the network set. The coding tree unit (CTU) is seen as the basic unit for the in-loop filtering processing. A CTU level control flag is applied in the sense of rate-distortion optimization (RDO). Extensive experimental results show that our ARLF approach obtains on average 2.17%, 2.65%, 2.58%, 2.51% under all-intra, low-delay P, low-delay, and random access configurations, respectively. Compared with other deep learning-based methods, the proposed approach can achieve better performance with low computation complexity.

中文翻译：

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基于自适应深度强化学习的 VVC 环路滤波器


最近，基于深度学习的环内滤波器在视频编码中的编码效率和主观质量方面得到了巨大的改进。然而，大多数现有的基于深度学习的环内滤波器往往会开发复杂的模型来换取良好的性能，并且它们对所有重建样本都采用单一的网络结构，这对各种视频内容缺乏足够的适应性，从而将其性能限制在在某种程度上。相比之下，本文提出了一种用于通用视频编码（VVC）的基于自适应深度强化学习的环路滤波器（ARLF）。具体来说，我们将过滤视为决策过程，并利用深度强化学习的最新进展，使用代理来选择合适的网络。为此，我们开发了一个轻量级主干，并利用它来设计一个包含不同复杂度网络的网络集 S\mathcal {S}。然后设计一个简单但高效的代理网络来从 S\mathcal {S} 预测最优网络，这使得模型能够适应各种视频内容。为了提高模型的鲁棒性，进一步提出了两阶段训练方案来训练代理并调整网络集。编码树单元（CTU）被视为环路滤波处理的基本单元。 CTU 级别控制标志在速率失真优化 (RDO) 的意义上应用。大量的实验结果表明，我们的 ARLF 方法在全帧内、低延迟 P、低延迟和随机接入配置下平均分别获得 2.17%、2.65%、2.58%、2.51%。与其他基于深度学习的方法相比，该方法可以以较低的计算复杂度实现更好的性能。