Sparse representations for spatial prediction and texture refinement

  • Authors:

  • Aurélie Martin;Jean-Jacques Fuchs;Christine Guillemot;Dominique Thoreau

  • Affiliations:

  • IRISA/Université de Rennes 1, Campus de Beaulieu, 35042 Rennes Cedex, France and Technicolor Research and Innovation labs, 1 avenue Belle Fontaine BP 19, 35510 Cesson-Sévigné Cedex, ...;IRISA/Université de Rennes 1, Campus de Beaulieu, 35042 Rennes Cedex, France;IRISA/Université de Rennes 1, Campus de Beaulieu, 35042 Rennes Cedex, France;Technicolor Research and Innovation labs, 1 avenue Belle Fontaine BP 19, 35510 Cesson-Sévigné Cedex, France

  • Venue:
  • Journal of Visual Communication and Image Representation
  • Year:
  • 2011

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Abstract

In this work, we propose a novel approach for signal prediction based on the use of sparse signal representations and Matching Pursuit (MP) techniques. The paper first focuses on spatial texture prediction in a conventional block-based hybrid coding scheme and secondly addresses inter-layer prediction in a scalable video coding (SVC) framework. For spatial prediction the signal reconstruction of the block to predict is based on basis functions selected with the MP iterative algorithm, to best match a causal neighborhood. Inter-layer MP based prediction employs base layer upsampled components additionally to the causal neighborhood in order to improve the representation of high frequencies. New solutions are proposed for efficiently deriving and exploiting the atoms dictionary through phase refinement and mono-dimensional basis functions. Experimental results indicate noticeable improvement of rate/distortion performance compared to the standard prediction methods as specified in H.264/AVC and its extension SVC.