Fast QTBT Partitioning Decision for Interframe Coding with Convolution Neural Network

Abstract

In the latest Joint Video Exploration Team (JVET) development, the quadtree plus binary tree (QTBT) block partition structure is proposed for more flexible block partitioning. Compared to the quadtree partitioning in HEVC, QTBT can achieve better compression performance at the expense of significantly increased encoding complexity. To address this issue, we propose a convolution neural network (CNN) oriented fast QTBT partitioning decision algorithm for inter coding. We analyze the QTBT in a statistical way, which effectively guides us to design the architecture of the CNN. Furthermore, the false prediction risk is controlled based on temporal correlation to improve the robustness of the scheme. Experimental results show that the proposed algorithm can speed up QTBT block partition structure by reducing 35% encoding time on average with only 0.55% increase in bit rate, which enables its applications in practical scenarios.