Fast CU Partition for VVC Using Texture Complexity Classification Convolutional Neural Network  

作　　者：Yue Zhang Pengyu Liu Xiaowei Jia Shanji Chen Tianyu Liu Chang Liu 

机构地区：[1]The Information Department,Beijing University of Technology,Beijing,100124,China [2]School of Physics and Electronic Information Engineering,Qinghai Minzu University,Xining,810000,China [3]Advanced Information Network Beijing Laboratory,Beijing,100124,China [4]Computational Intelligence and Intelligent Systems Beijing Key Laboratory,Beijing,100124,China [5]Department of Computer Science,University of Pittsburgh,Pittsburgh,15260,USA

出　　处：《Computers, Materials & Continua》2022年第11期3545-3556,共12页计算机、材料和连续体（英文）

基　　金：This paper is supported by the following funds:The National Key Research and Development Program of China(2018YFF01010100);Basic Research Program of Qinghai Province under Grants No.2021-ZJ-704,The Beijing Natural Science Foundation(4212001);Advanced information network Beijing laboratory(PXM2019_014204_500029).

摘　　要：Versatile video coding(H.266/VVC),which was newly released by the Joint Video Exploration Team(JVET),introduces quad-tree plus multitype tree(QTMT)partition structure on the basis of quad-tree(QT)partition structure in High Efficiency Video Coding(H.265/HEVC).More complicated coding unit(CU)partitioning processes in H.266/VVC significantly improve video compression efficiency,but greatly increase the computational complexity compared.The ultra-high encoding complexity has obstructed its real-time applications.In order to solve this problem,a CU partition algorithm using convolutional neural network(CNN)is proposed in this paper to speed up the H.266/VVC CU partition process.Firstly,64×64 CU is divided into smooth texture CU,mildly complex texture CU and complex texture CU according to the CU texture characteristics.Second,CU texture complexity classification convolutional neural network(CUTCC-CNN)is proposed to classify CUs.Finally,according to the classification results,the encoder is guided to skip different RDO search process.And optimal CU partition results will be determined.Experimental results show that the proposed method reduces the average coding time by 32.2%with only 0.55%BD-BR loss compared with VTM 10.2.

关 键 词：Versatile video coding(VVC) coding unit partition convolutional neural network(CNN) 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]