000190730 001__ 190730
000190730 005__ 20180913062147.0
000190730 037__ $$aCONF
000190730 245__ $$aGraph-Based vs Depth-Based Data Representation for Multiview Images
000190730 269__ $$a2013
000190730 260__ $$c2013
000190730 336__ $$aConference Papers
000190730 520__ $$aIn this paper, we propose a representation and coding method for multiview images. As an alternative to depth-based schemes, we propose a representation that captures the geometry and the dependencies between pixels in different views in the form of connections in a graph. In our approach it is possible to perform compression of the geometry information and to preserve a direct control of the effect of geometry approximation on view reconstruction. This is not possible with classical depth-based representations. As a results, our method leads to more accurate view prediction, when compared to conventional lossy coding of depth maps operating at the same bit rate. We finally show in experiments that our representation adapts the amount of transmitted geometry to the complexity of the predictions that are performed at the decoder.
000190730 700__ $$aMaugey, Thomas
000190730 700__ $$aOrtega, Antonio
000190730 700__ $$0241061$$aFrossard, Pascal$$g101475
000190730 7112_ $$aAsilomar Conference on Signals, Systems, and Computers$$cPacific Grove, CA, USA$$dNovember 3-6
000190730 8564_ $$s667121$$uhttps://infoscience.epfl.ch/record/190730/files/20131126025659_503002_1139.pdf$$yn/a$$zn/a
000190730 909C0 $$0252393$$pLTS4$$xU10851
000190730 909CO $$ooai:infoscience.tind.io:190730$$pconf$$pSTI
000190730 917Z8 $$x206186
000190730 937__ $$aEPFL-CONF-190730
000190730 973__ $$aEPFL$$rREVIEWED$$sACCEPTED
000190730 980__ $$aCONF