Article thumbnail

WynerZiv coding of multiview images with unsupervised learning of disparity and

By David Chen, David Varodayan, Markus Flierl and Bernd Girod

Abstract

Wyner-Ziv coding of multiview images avoids communications between source cameras. To achieve good compression performance, the decoder must relate the source and side information images. Since correlation between the two images is exploited at the bit level, it is desirable to map small Euclidean distances between coefficients into small Hamming distances between bitwise codewords. This important mapping property is not achieved with the binary code but can be achieved with the Gray code. Comparing the two mappings, it is observed that the Gray code offers a substantial benefit for unsupervised learning of unknown disparity but provides limited advantage if disparity is known. Experimental results with multiview images demonstrate the Gray code achieves PSNR gains of 2 dB over the binary code for unsupervised learning of disparity. Index Terms — stereo vision, multiview images, Gray code 1

Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.208.797
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.stanford.edu/%7Edmc... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.