Skip to search boxSkip to navigationSkip to main content

Bit allocation and encoded view selection for optimal multiview image representation

  • National Institute of Informatics
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

Novel coding tools have been proposed recently to encode texture and depth maps of multiview images, exploiting inter-view correlations, for depth-image-based rendering (DIBR). However, the important associated bit allocation problem for DIBR remains open: for chosen view coding and synthesis tools, how to allocate bits among texture and depth maps across encoded views, so that the fidelity of a set of V views reconstructed at the decoder is maximized, for a fixed bitrate budget? In this paper, we present an optimization strategy to select subset of texture and depth maps of the original V views for encoding at appropriate quantization levels, so that at the decoder, the combined quality of decoded views (using encoded texture maps) and synthesized views (using encoded texture and depth maps of neighboring views) is maximized. We show that using the monotonicity property, complexity of our strategy can be greatly reduced. Experiments show that using our strategy, one can achieve up to 0.83dB gain in PSNR improvement over a heuristic scheme of encoding only texture maps of all V views at constant quantization levels. Further, computation can be reduced by up to 66% over a full parameter search approach.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Publication milestones

  • Published - 01/01/2010

Publication status

Published - 01/01/2010

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781424481101

ISBN (Electronic)

9781424481101

Publication IDs

  • handle.net: 10547/292588
  • Scopus: 78650917138

Host publication title

nan

Access to documents

Publication metrics