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  • Research Article
  • Open Access

Scalable Video Coding with Interlayer Signal Decorrelation Techniques

EURASIP Journal on Advances in Signal Processing20072007:054342

https://doi.org/10.1155/2007/54342

  • Received: 12 September 2006
  • Accepted: 20 February 2007
  • Published:

Abstract

Scalability is one of the essential requirements in the compression of visual data for present-day multimedia communications and storage. The basic building block for providing the spatial scalability in the scalable video coding (SVC) standard is the well-known Laplacian pyramid (LP). An LP achieves the multiscale representation of the video as a base-layer signal at lower resolution together with several enhancement-layer signals at successive higher resolutions. In this paper, we propose to improve the coding performance of the enhancement layers through efficient interlayer decorrelation techniques. We first show that, with nonbiorthogonal upsampling and downsampling filters, the base layer and the enhancement layers are correlated. We investigate two structures to reduce this correlation. The first structure updates the base-layer signal by subtracting from it the low-frequency component of the enhancement layer signal. The second structure modifies the prediction in order that the low-frequency component in the new enhancement layer is diminished. The second structure is integrated in the JSVM 4.0 codec with suitable modifications in the prediction modes. Experimental results with some standard test sequences demonstrate coding gains up to 1 dB for I pictures and up to 0.7 dB for both I and P pictures.

Keywords

  • Pyramid
  • Quantum Information
  • Lower Resolution
  • Base Layer
  • Test Sequence

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Authors’ Affiliations

(1)
Institut de Recherche en Informatique et Systèmes Aléatoires, Institut National de Recherche en Informatique et en Automatique, Rennes Cedex, 35042, France

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Copyright

© Wenxian Yang et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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