Machine learning‐based H.264/AVC to HEVC transcoding via motion information reuse and coding mode similarity analysis. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Lin, Hongwei; He, Xiaohai; Qing, Linbo; Su, Shan; Xiong, Shuhua
• Is Part Of: IET image processing. Volume 13:Issue 1(2019)
• Page Start: 34
• Page End: 43
• Publication Date: 2019-01-01
• Subjects: learning (artificial intelligence) -- transcoding -- video coding -- code standards -- Bayes methods -- feature selection -- image classification -- trees (mathematics) -- computational complexity -- image motion analysis; motion information reuse -- coding mode similarity analysis -- high‐efficiency video coding -- video resources -- HEVC transcoders -- updated standard -- HEVC transcoding scheme -- optimised tree‐augmented Naive Bayesian approach -- HEVC coding unit depth -- motion vector calculation method -- machine learning -- H.264 standard -- AVC standard -- three‐level classifier -- feature selection method -- coding complexity reduction -- transcoding algorithms; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5703
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : High‐efficiency video coding (HEVC), which is the latest video coding standard, is expected to have a dominant position in the market in the near future. However, most video resources are now encoded using the H.264/AVC standard. Consequently, there is a growing need for fast H.264/AVC to HEVC transcoders to facilitate the migration to the updated standard. This paper proposes a fast H.264/AVC to HEVC transcoding scheme, which constructs a three‐level classifier using an optimised tree‐augmented Naive Bayesian approach to predict the HEVC coding unit depth. A feature selection method is then proposed to improve prediction accuracy. A motion vector (MV) calculation method is also proposed to reduce the complexity of MV prediction in HEVC by reusing MVs from H.264/AVC. Experimental results show that, compared with other state‐of‐the‐art transcoding algorithms, the proposed algorithm considerably reduces coding complexity while causing only negligible rate‐distortion degradation.