000130365 001__ 130365
000130365 005__ 20190316234439.0
000130365 02470 $$2ISI$$a000273295700014
000130365 037__ $$aCONF
000130365 245__ $$aLow bit-rate compression of omnidirectional images
000130365 260__ $$c2009
000130365 269__ $$a2009
000130365 336__ $$aConference Papers
000130365 520__ $$aOmnidirectional images represent a special type of images that are captured by vision sensors with a 360-degree field of view. This work targets the compression of such images by taking into account their particular geometry. We first map omnidirectional images to spherical ones and then perform sparse image decomposition over a dictionary of geometric atoms on the 2D sphere. A coder based on Matching Pursuit and adaptive quantization is finally proposed for efficient compression of the omnidirectional images. The experiments demonstrate that proposed system outperforms the JPEG2000 coding of unfolded images. As most of omnidirectional sensors can be parametrized with a spherical camera model, the proposed method is generic with respect to different sensor constructions.
000130365 6531_ $$aomnidirectional
000130365 6531_ $$acompression
000130365 6531_ $$asparse
000130365 6531_ $$aLTS4
000130365 700__ $$0240452$$aTosic, I.$$g163024
000130365 700__ $$0241061$$aFrossard, P.$$g101475
000130365 7112_ $$aPCS$$cChicago, USA$$dMay 6-8, 2009
000130365 773__ $$tProceedings of PCS
000130365 8564_ $$zURL
000130365 8564_ $$s353204$$uhttps://infoscience.epfl.ch/record/130365/files/omni_cr.pdf$$zn/a
000130365 909C0 $$0252393$$pLTS4$$xU10851
000130365 909CO $$ooai:infoscience.tind.io:130365$$pconf$$pSTI$$qGLOBAL_SET
000130365 937__ $$aEPFL-CONF-130365
000130365 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000130365 980__ $$aCONF