Fast 3D‐HEVC inter coding using data mining and machine learning. Issue 11 (2nd June 2022)

• Record Type: Journal Article
• Authors: Zhang, Ruyi; Jia, Kebin; Yu, Yuan; Liu, Pengyu; Sun, Zhonghua
• Is Part Of: IET image processing. Volume 16:Issue 11(2022)
• Page Start: 3067
• Page End: 3084
• Publication Date: 2022-06-02
• Subjects: Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12539
• Languages: English
• ISSNs: 1751-9659

Abstract

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.