Fast QTMT Partition for VVC Intra Coding Using U-Net Framework

2023/04/06 by Zan, Zhao, Huang, Leilei, Chen, ShuShi +4
#FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Multimedia (cs.MM) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2304.03076

Abstract

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%.

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