Distributed coding of random dot stereograms with unsupervised learning of disparity

David Varodayan, Aditya Mavlankar, Markus Flierl, Bernd Girod

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

Abstract

Distributed compression is particularly attractive for stereoscopic images since it avoids communication between cameras. Since compression performance depends on exploiting the redundancy between images, knowing the disparity is important at the decoder. Unfortunately, distributed encoders cannot calculate this disparity and communicate it. We consider a toy problem, the compression of random dot stereograms, and propose an Expectation Maximization algorithm to perform unsupervised learning of disparity during the decoding procedure. Our experiments show that this can achieve twice as efficient compression compared to a system with no disparity compensation and perform nearly as well as a system which knows the disparity through an oracle.

Original languageEnglish (US)
Title of host publication2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006
PublisherIEEE Computer Society
Pages5-8
Number of pages4
ISBN (Print)0780397517, 9780780397514
DOIs
StatePublished - 2006
Event2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006 - Victoria, BC, Canada
Duration: Oct 3 2006Oct 6 2006

Publication series

Name2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006

Conference

Conference2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006
CountryCanada
CityVictoria, BC
Period10/3/0610/6/06

ASJC Scopus subject areas

  • Signal Processing

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