Open AccessFast 3D‐HEVC inter coding using data mining and machine learning
Author(s) -
Zhang Ruyi,
Jia Kebin,
Yu Yuan,
Liu Pengyu,
Sun Zhonghua
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12539
Subject(s) - coding tree unit , computer science , context adaptive binary arithmetic coding , coding (social sciences) , context adaptive variable length coding , artificial intelligence , tunstall coding , algorithmic efficiency , variable length code , multiview video coding , coding gain , data compression , computer vision , algorithm , pattern recognition (psychology) , decoding methods , video tracking , mathematics , video processing , statistics
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.