Open AccessMachine learning‐based H.264/AVC to HEVC transcoding via motion information reuse and coding mode similarity analysis
Author(s) -
Lin Hongwei,
He Xiaohai,
Qing Linbo,
Su Shan,
Xiong Shuhua
Publication year - 2019
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/iet-ipr.2018.5703
Subject(s) - computer science , coding tree unit , algorithmic efficiency , coding (social sciences) , context adaptive variable length coding , motion vector , artificial intelligence , quarter pixel motion , algorithm , transcoding , computational complexity theory , context adaptive binary arithmetic coding , computer vision , motion estimation , real time computing , data compression , decoding methods , mathematics , computer network , statistics , image (mathematics)
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.