Fast CU partition strategy for HEVC intra-frame Coding using learning approach via random forests
Author(s)
Author(s)
Du, B.
Yang, X.
Date Issued
2015
Publisher
IEEE
Related Publication(s)
2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Proceedings
Start page
1093
End page
1098
Abstract
HEVC (High Efficiency Video Coding) achieves cutting edge encoding efficiency and outperforms previous standards, such as the H.264/AVC. One of the key contributions to the improvement is the intra-frame coding that employs abundant coding unit (CU) sizes. However finding the optimal CU size is computationally expensive. To alleviate the intra encoding complexity and facilitate the real-time implementation, we use a machine learning technique: the random forests, for training. Based on off-line training, we propose using the forest classifier to skip or terminate the current CU depth level. In addition, neighboring CU size decisions are utilized to determine the current depth range. Experimental results show that our proposed algorithm can achieve 48.31% time reduction, with 0.80% increase in the Bjantegaard delta bitrate (BD-rate), which are state-of-the-art results compared with all algorithms in the literature.
SFU Affiliated Publication
No
Availability at SFU Library
No database links found.