Research Article
A Machine Learning Method for Optimizing Partition of Prediction Block in Coding Unit in H.265/HEVC
@INPROCEEDINGS{10.4108/eai.27-8-2020.2297985, author={Wenchan Jiang and Ming Yang and Ying Xie and Zhigang Li}, title={A Machine Learning Method for Optimizing Partition of Prediction Block in Coding Unit in H.265/HEVC}, proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace}, publisher={EAI}, proceedings_a={MOBIMEDIA}, year={2020}, month={11}, keywords={machine learning cnn ctu partitioning h265/hevc coding unit}, doi={10.4108/eai.27-8-2020.2297985} }- Wenchan Jiang
Ming Yang
Ying Xie
Zhigang Li
Year: 2020
A Machine Learning Method for Optimizing Partition of Prediction Block in Coding Unit in H.265/HEVC
MOBIMEDIA
EAI
DOI: 10.4108/eai.27-8-2020.2297985
Abstract
In the latest generation of video coding standard – H.265/HEVC, the partition of Coding Tree Unit (CTU) into CU (Coding Unit) is a very critical yet time-consuming component. Traditional methods find the optimum partition mode for each CTU through iterative and exhaustive search, which is a very time-consuming process and hinders its application to real-time video streaming scenarios. In this research, we explored and implemented a machine learning based method to avoid the exhaustive search and to improve the performance of encode/decode by optimizing the partition of prediction block in the coding unit. Our results in coding unit split pattern prediction show a significant performance improvement in terms of processing time.
