Title
Fast 3D-HEVC inter coding using data mining and machine learning
Abstract
The Three-Dimensional High Efficiency Video Coding standard is a video compression standard developed based on the two-dimensional video coding standard HEVC and used to encode multi-view plus depth format video. This paper proposes an algorithm based on eXtreme Gradient Boosting to solve the problem of high inter-frame coding complexity in 3D-HEVC. Firstly, explore the correlation between the division depth of the inter-frame coding unit and the texture features in the map, as well as the correlation between the coding unit division structure between each map and each viewpoint. After that, based on the machine learning method, a fast selection mechanism for dividing the depth range of the inter-frame coding tree unit based on the eXtreme Gradient Boosting algorithm is constructed. Experimental results show that, compared with the reference software HTM-16.0, this method can save an average of 35.06% of the coding time, with negligible degradation in terms of coding performance. In addition, the proposed algorithm has achieved different degrees of improvement in coding performance compared with the related works.
Year
DOI
Venue
2022
10.1049/ipr2.12539
IET IMAGE PROCESSING
DocType
Volume
Issue
Journal
16
11
ISSN
Citations 
PageRank 
1751-9659
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Ruyi Zhang100.34
Bin Jiang212.38
Yuan Yu300.34
Pengyu Liu400.34
Zhonghua Sun57526.21