TY - GEN
T1 - Fast QTBT Partitioning Decision for Interframe Coding with Convolution Neural Network
AU - Wang, Zhao
AU - Wang, Shiqi
AU - Zhang, Xinfeng
AU - Wang, Shanshe
AU - Ma, Siwei
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/29
Y1 - 2018/8/29
N2 - 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.
AB - 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.
KW - Block partitioning
KW - CNN
KW - Quadtree plus binary tree
KW - Video coding
UR - https://www.scopus.com/pages/publications/85062902278
U2 - 10.1109/ICIP.2018.8451258
DO - 10.1109/ICIP.2018.8451258
M3 - 会议稿件
AN - SCOPUS:85062902278
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2550
EP - 2554
BT - 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PB - IEEE Computer Society
T2 - 25th IEEE International Conference on Image Processing, ICIP 2018
Y2 - 7 October 2018 through 10 October 2018
ER -