  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%.
