The Versatile Video Coding (VVC) standard has been finalized by Joint Video
Exploration Team (JVET) in 2020. Compared to the High Efficiency Video Coding
(HEVC) standard, VVC offers about 50% compression efficiency gain, in terms of
Bjontegaard Delta-Rate (BD-rate), at the cost of about 10x more encoder
complexity. In this paper, we propose a Convolutional Neural Network
(CNN)-based method to speed up inter partitioning in VVC. Our method operates
at the Coding Tree Unit (CTU) level, by splitting each CTU into a fixed grid of
8x8 blocks. Then each cell in this grid is associated with information about
the partitioning depth within that area. A lightweight network for predicting
this grid is employed during the rate-distortion optimization to limit the
Quaternary Tree (QT)-split search and avoid partitions that are unlikely to be
selected. Experiments show that the proposed method can achieve acceleration
ranging from 17% to 30% in the RandomAccess Group Of Picture 32 (RAGOP32) mode
of VVC Test Model (VTM)10 with a reasonable efficiency drop ranging from 0.37%
to 1.18% in terms of BD-rate increase.

本文提出了一种基于卷积神经网络（CNN）的方法来加速 VVC 中的分区，该方法在编码树单元（CTU）级别操作，将每个 CTU 分割成 8x8 个固定网格块，每个网格中的单元格与该区域内的分区深度相关联，带有轻量级的网络用于预测此网格，通过限制四进制树（QT）分割搜索并避免不太可能选中的分区来达到加速的效果。实验证明，该方法在 VVC Test Model (VTM) 10 的 RandomAccess Group Of Picture 32（RAGOP32）模式中，可以实现从 17% 到 30% 的加速，以及以 0.37% 到 1.18% 的 BD 率增加的合理效率下降。