Optimizing H.264/AVC interprediction on a GPU-based framework

  • Authors:

  • Rafael Rodríguez-Sánchez;José Luis Martínez;Gerardo Fernández-Escribano;José L. Sánchez;José M. Claver;Pedro Diaz

  • Affiliations:

  • Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Avenida de España s/n, 02071, Albacete, Spain;Architecture and Technology of Computing Systems Group, Complutense University, Madrid, Spain;Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Avenida de España s/n, 02071, Albacete, Spain;Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Avenida de España s/n, 02071, Albacete, Spain;Departamento de Informática, Universidad de Valencia, Avenida de Vicente Andrés Estellés, s/n,46100 Burjassot, Valencia, Spain;Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Avenida de España s/n, 02071, Albacete, Spain

  • Venue:
  • Concurrency and Computation: Practice & Experience
  • Year:
  • 2012

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Abstract

H.264/MPEG-4 part 10 is the latest standard for video compression and promises a significant advance in terms of quality and distortion compared with the commercial standards currently most in use such as MPEG-2 or MPEG-4. To achieve this better performance, H.264 adopts a large number of new/improved compression techniques compared with previous standards, albeit at the expense of higher computational complexity. In addition, in recent years new hardware accelerators have emerged, such as graphics processing units (GPUs), which provide a new opportunity to reduce complexity for a large variety of algorithms. However, current GPUs suffer from higher power consumption requirements because of its design. Up to now, GPU-based software developers have not taken this into account. In this paper, we present a detailed procedure to implement the H.264 motion estimation for a GPU, with the aim of reducing time and, as a consequence, the energy consumption. The results show a negligible drop in rate distortion with a time reduction of over 91.5% on average and it reduces the energy consumption by a factor of 11.78 compared with the reference implementation. Copyright © 2011 John Wiley & Sons, Ltd.