Wyner-Ziv coding of multiview images with unsupervised learning of two disparities

David Chen, David Varodayan, Markus Flierl, Bernd Girod

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Wyner-Ziv coding of multiview images is an attractive solution because it avoids communications between individual cameras. To achieve good rate-distortion performance, the Wyner-Ziv decoder must reliably estimate the disparities between the multiview images. For the scenario where two reference images exist at the decoder, we propose a codec that effectively performs unsupervised learning of the two disparities between an image being Wyner-Ziv coded and the two reference images. The proposed two-disparity decoder disparity-compensates the two references images and generates side information more accurately than an existing one-disparity decoder. Experimental results with real multiview images demonstrate that the proposed codec achieves PSNR gains of 1-5 dB over the onedisparity codec.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Proceedings
Pages629-632
Number of pages4
DOIs
StatePublished - Oct 23 2008
Externally publishedYes
Event2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Hannover, Germany
Duration: Jun 23 2008Jun 26 2008

Publication series

Name2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Proceedings

Other

Other2008 IEEE International Conference on Multimedia and Expo, ICME 2008
Country/TerritoryGermany
CityHannover
Period6/23/086/26/08

Keywords

  • Data compression
  • Disparity
  • Image coding
  • Stereo vision

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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