Fast Intra CU Size Decision for HEVC Based on Machine Learning
Authors
Meng Wang, Junru Li, Xiaodong Xie, Yuan Li, Huizhu Jia
Corresponding Author
Meng Wang
Available Online May 2018.
- 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/).
Cite this article
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 -