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：通用视频编码（VVC）大幅提升了编码效率，但代价是引入了众多复杂的编码工具，尤其是灵活的四边形加多类型树（QTMT）块划分。本文提出一种基于深度学习的算法，用于VVC帧内编码的快速QTMT划分。该方案通过提前终止低概率帧内预测与划分操作，在BD-BR增幅极小的情况下显著减少编码时间。首先，采用重新设计的U-Net作为网络基础框架；其次，设计质量参数（QP）融合网络以调节QP对划分结果的影响；最后，采用优化的后处理策略以平衡编码性能与复杂度。实验结果表明，与当前最优方案相比，本方案实现了44.74%至68.76%的复杂度降低，BD-BR增幅仅为0.60%至2.33%。