Rate-Distortion Optimized Graph-Based Representation for Multiview Images With Complex Camera Configurations

Graph-based representation (GBR) has recently been proposed for describing color and geometry of multiview video content. The graph vertices represent the color information, while the edges represent the geometry information, i.e., the disparity, by connecting corresponding pixels in two camera view...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 26(2017), 6 vom: 16. Juni, Seite 2644-2655
1. Verfasser: Su, Xin (VerfasserIn)
Weitere Verfasser: Maugey, Thomas, Guillemot, Christine
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2017
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
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520 |a Graph-based representation (GBR) has recently been proposed for describing color and geometry of multiview video content. The graph vertices represent the color information, while the edges represent the geometry information, i.e., the disparity, by connecting corresponding pixels in two camera views. In this paper, we generalize the GBR to multiview images with complex camera configurations. Compared with the existing GBR, the proposed representation can handle not only horizontal displacements of the cameras but also forward/backward translations, rotations, etc. However, contrary to the usual disparity that is a 2-D vector (denoting horizontal and vertical displacements), each edge in GBR is represented by a 1-D disparity. This quantity can be seen as the disparity along an epipolar segment. In order to have a sparse (i.e., easy to code) graph structure, we propose a rate-distortion model to select the most meaningful edges. Hence the graph is constructed with "just enough" information for rendering the given predicted view. The experiments show that the proposed GBR allows high reconstruction quality with lower or equivalent coding rate than traditional depth-based representations 
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700 1 |a Guillemot, Christine  |e verfasserin  |4 aut 
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