Light-weight CNN-based VVC Inter Partitioning Acceleration
Yiqun Liu Mohsen Abdoli Thomas Guionnet Christine Guillemot Aline Roumy
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.