Fast Partitioning Decision Making for Prediction Units on H.264-to-HEVC Transcoding Using Machine Learning

  • Yan Soares UFPel
  • Guilherme Corrêa UFPel
  • Luciano Agostini UFPel

Resumo


With the high computational complexity of transcoding video from the H.264/AVC standard to the state-of-the-art High Efficiency Video Coding (HEVC) standard, new approaches must be explored to fasten up the adaptation of all legacy content. This work proposes a fast decision algorithm to reduce the transcoding complexity between the H.264/AVC and HEVC video standards using partitioning information from the H.264/AVC macroblocks to fasten up the partitioning decisions of Prediciton Units (PUs) on the HEVC reencoding process. This strategy allowed a 25% reduction on transcoding time with a compression efficiency loss of just 0.745% in comparison with the original transcoder.
Publicado
29/10/2019
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SOARES, Yan; CORRÊA, Guilherme; AGOSTINI, Luciano. Fast Partitioning Decision Making for Prediction Units on H.264-to-HEVC Transcoding Using Machine Learning. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA) , 2019, Rio de Janeiro. Anais do XXV Simpósio Brasileiro de Multimídia e Web. Porto Alegre: Sociedade Brasileira de Computação, oct. 2019 . p. 161-168.