Fast Intra CU Size Decision for HEVC Based on Machine Learning

DOI

10.2991/amcce-18.2018.132How to use a DOI?

Keywords

HEVC, Intra prediction, CU size, Fast algorithm.

Abstract

High Efficiency Video Coding (HEVC) is the new generation of video coding standard. A quad-tree based Coding Unit (CTU) partitioning scheme is used to adapt to different video contents. However, it brings the dramatically increasing of coding complexity because there are a large amount of CU partition structure to traverse. In this paper, we proposed a fast CU size decision method based on machine learning. CU features is extracted and Support Vector Machine (SVM) model is trained to classify CU splitting or non-splitting. Experimental results show that our proposed method can achieve 40.23% encoding time saving on average and the BD-rate loss is only 0.83% under All Intra (AI) configuration.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Meng Wang
AU  - Junru Li
AU  - Xiaodong Xie
AU  - Yuan Li
AU  - Huizhu Jia
PY  - 2018/05
DA  - 2018/05
TI  - Fast Intra CU Size Decision for HEVC Based on Machine Learning
BT  - Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SP  - 756
EP  - 761
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.132
DO  - 10.2991/amcce-18.2018.132
ID  - Wang2018/05
ER  -