A VVC intra coding method based on fast partition for coding unit

Abstract

Compared to the high efficiency video coding (HEVC) standard, the latest generation coding standard, versatile video coding (VVC) has introduced many new technologies, including quadtree (QT) and multi-type tree (MTT) partitioning. MTT partition is extended from QT partition in HEVC. The new partition method increases encoding complexity, leading to a sharp increase in encoding time. To reduce encoding complexity, a fast intra coding method combining deep learning methods and early decision in the MTT direction was proposed. Firstly, a light-weight convolutional neural network (CNN) network was used to predict partition for QT and partial MTT. Then, an early prediction for MTT partition direction method was adopted for further optimization of residual MTT. Experimental results show that the proposed method can significantly reduce encoding complexity, with a 74.3% reduction in encoding time compared to the original encoder with only 3.3% rate loss. Moreover, the performance of proposed method is superior to other comparative algorithms.

Keywords

• VVC;intra coding;convolutional neural network;fast coding;quadtree;multi-type tree