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Coding mode decision algorithm for fast HEVC transrating using heuristics and machine learning

Grellert, M. ; Cruz, L. A. S. C. ; Zatt, B. ; Bampi, S.

Journal of Real-Time Image Processing Vol. 18, Nº 6, pp. 1881 - 1896, December, 2021.

ISSN (print): 1861-8200
ISSN (online): 1861-8219

Scimago Journal Ranking: 0,52 (in 2021)

Digital Object Identifier: 10.1007/s11554-020-01063-x

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This article describes a framework to speed up the HEVC encoding decisions for on-demand transrating of bitstreams. The methods proposed collect information from a high-quality reference bitstream which after processing is used to limit the number of modes evaluated in subsequent re-encodings at different bitrates. In this way, the time required to process re- encode-time computing-intensive decisions, such as partitioning and motion estimation is significantly reduced. The methods proposed are a combination of heuristics with a statistical basis and fast decision techniques trained using automatic learn- ing methodologies. Experimental results using the HEVC reference encoder show that jointly the methods proposed reduce the transcoding computational complexity by up to 78.8%, with Bjontegaard bitrate deltas penalties smaller than 1.06%. A comparison with related works showed that the proposed method is able to outperform state-of-the-art solutions in terms of combined rate-distortion–complexity performance indicators.