Fast mode decision algorithm for H.264/AVC-to-SVC transcoding with temporal scalability

  • Authors:

  • Rosario Garrido-Cantos;Jan De Cock;Sebastiaan Van Leuven;Pedro Cuenca;Antonio Garrido;Rik Van de Walle

  • Affiliations:

  • Albacete Research Institute of Informatics, University of Castilla-La Mancha, Albacete, Spain;Department of Electronics and Information Systems - Multimedia Lab, Ghent University - IBBT, Ghent, Belgium;Department of Electronics and Information Systems - Multimedia Lab, Ghent University - IBBT, Ghent, Belgium;Albacete Research Institute of Informatics, University of Castilla-La Mancha, Albacete, Spain;Albacete Research Institute of Informatics, University of Castilla-La Mancha, Albacete, Spain;Department of Electronics and Information Systems - Multimedia Lab, Ghent University - IBBT, Ghent, Belgium

  • Venue:
  • MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
  • Year:
  • 2012

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Abstract

Scalable Video Coding (SVC) uses a notion of layers within the encoded bitstream for providing temporal, spatial and quality scalability, separately or combined. By truncating layers the bitstream can be adapted to devices with different characteristics and to varying network constraints. Since the majority of the existing video content is encoded using H.264/AVC without scalability, they cannot benefit from these scalability tools, so a transcoding process should be applied to provide scalability to this existing encoded content. In this paper, an algorithm based on Machine Learning techniques for temporal scalability transcoding from H.264/AVC to SVC focusing on mode decision task is discussed. The results show that when our technique is applied, the complexity is reduced by 82% while maintaining coding efficiency.