Versatile Video Coding (VVC) has significantly increased encoding efficiency at the expense of numerous complex coding tools, particularly the flexible Quad-Tree plus Multi-type Tree (QTMT) block partition. This paper proposes a deep learning-based algorithm applied in fast QTMT partition for VVC intra coding. Our solution greatly reduces encoding time by early termination of less-likely intra prediction and partitions with negligible BD-BR increase. Firstly, a redesigned U-Net is recommended as the network's fundamental framework. Next, we design a Quality Parameter (QP) fusion network to regulate the effect of QPs on the partition results. Finally, we adopt a refined post-processing strategy to better balance encoding performance and complexity. Experimental results demonstrate that our solution outperforms the state-of-the-art works with a complexity reduction of 44.74% to 68.76% and a BD-BR increase of 0.60% to 2.33%.
翻译：摘要： 通用视频编码（VVC）显着提高了编码效率，但牺牲了许多复杂的编码工具，特别是灵活的四叉树加多类型树（QTMT）块分区。本文提出了一种基于深度学习的算法，应用于VVC内部编码的快速QTMT分区。我们的解决方案通过提前终止不太可能的内部预测和分区来极大地减少编码时间，并且分区对BD-BR的增加可以忽略不计。首先，我们推荐采用重新设计的U-Net作为网络的基本框架。接下来，我们设计一种质量参数（QP）融合网络来调节QP对分区结果的影响。最后，我们采用了一种改进的后处理策略，以更好地平衡编码性能和复杂性。实验结果表明，我们的解决方案优于现有的最先进的作品，复杂性减少了44.74%至68.76%，BD-BR增加了0.60%至2.33%。
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