TY  - CONF
ID  - SisLab4659
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4659/
A1  - Hoang, Van Xiem
A1  - Nguyen, Quang Sang
A1  - Dinh, Bao Minh
A1  - Do, Ngoc Minh
A1  - Dinh, Trieu Duong
Y1  - 2021/10//
N2  - Versatile Video Coding (VVC) has been standardization in July 2020. Compared to previous High Efficiency Video Coding (HEVC) standard, VVC saves up to 50% bitrate for equal perceptual video quality. To reach this efficiency, Joint Video Experts Team (JVET) has introduced a number of improvement techniques to VVC model. As a result, the complexity of VVC encoding also greatly increases. One of
the new techniques affects to the growing of complexity is the quad-tree nested multi-type tree (QTMT) including binary split and ternary splits, which lead to a block in VVC with various shapes in both square and rectangle. Based on the
aforementioned information we propose in this paper a new deep learning based fast QTMT method. We use a learned
convolutional neural network (CNN) model namely EarlyTerminated Hierarchical CNN to predict the coding unit map and then fed into the VVC encoder to early terminate the block partitioning process. Experimental results show that the proposed method can save 30.29% encoding time with a
negligible BD-Rate increase.
TI  - Fast QTMT for H.266/VVC Intra Prediction using Early-Terminated Hierarchical CNN model
SP  - 195
M2  - HoChiMinh city, Vietnam
AV  - public
EP  - 200
T2  - International Conference on Advanced Technologies for Communications
ER  -