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• W2893688436 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." @default.
• W2893688436 created "2018-10-05" @default.
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• W2893688436 date "2019-01-01" @default.
• W2893688436 modified "2023-10-12" @default.
• W2893688436 title "Machine learning‐based H.264/AVC to HEVC transcoding via motion information reuse and coding mode similarity analysis" @default.
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• W2893688436 doi "https://doi.org/10.1049/iet-ipr.2018.5703" @default.
• W2893688436 hasPublicationYear "2019" @default.
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