000138497 001__ 138497
000138497 005__ 20190117210912.0
000138497 037__ $$aCONF
000138497 245__ $$aDistributed multi-view image coding with learned dictionaries
000138497 260__ $$c2009
000138497 269__ $$a2009
000138497 336__ $$aConference Papers
000138497 500__ $$aInvited paper
000138497 520__ $$aThis paper addresses the problem of distributed image coding in camera neworks. The correlation between multiple images of a scene captured from different viewpoints can be effiiciently modeled by local geometric transforms of prominent images features. Such features can be efficiently represented by sparse approximation algorithms using geometric dictionaries of various waveforms, called atoms. When the dictionaries are built on geometrical transformations of some generating functions, the features in different images can be paired with simple local geometrical transforms, such as scaling, rotation or translations. The construction of the dictionary however represents a trade-off between approximation performance that generally improves with the size of the dictionary, and cost for coding the atoms indexes. We propose a learning algorithm for the construction of dictionaries adapted to stereo omnidirectional images. The algorithm is based on a maximum likelihood solution that results in atoms adapted to both image approximation and stereo matching. We then use the learned dictionary in a Wyner-Ziv multi-view image coder built on a geometrical correlation model. The experimental results show that the learned dictionary improves the rate- distortion performance of the Wyner-Ziv coder at low bit rates compared to a baseline parametric dictionary.
000138497 6531_ $$aLTS4
000138497 6531_ $$aDistributed source coding
000138497 6531_ $$asparse approximations
000138497 6531_ $$amulti-view images
000138497 700__ $$0240452$$aTosic, Ivana$$g163024
000138497 700__ $$0241061$$aFrossard, Pascal$$g101475
000138497 7112_ $$aMobiMedia 2009$$cLondon
000138497 773__ $$tProceedings of Mobimedia
000138497 8564_ $$zURL
000138497 8564_ $$s392238$$uhttps://infoscience.epfl.ch/record/138497/files/DSClearn_cr.pdf$$zn/a
000138497 909C0 $$0252393$$pLTS4$$xU10851
000138497 909CO $$ooai:infoscience.tind.io:138497$$pconf$$pSTI
000138497 937__ $$aEPFL-CONF-138497
000138497 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000138497 980__ $$aCONF