Graph-Based Representation and Coding of Multiview Images

Instead of lossily coding depth images resulting in undesirable geometric distortion, graph-based representation (GBR) describes disparity information as a graph with a controllable accuracy. In this paper, we propose a more compact graphical representation called GBR-plus to code both disparity and color information of a target view given a reference view. Specifically, first we differentiate between disocclusion holes (occluded spatial regions in the reference view) and rounding holes (insufficiently sampled regions in the reference view) in the synthesized target view, so that the decoder can optionally complete rounding holes via signal interpolation without coding overhead. Second, we use a compact graphical representation to delimit disparity-shifted boundaries of objects in the target view, which is coded losslessly. Finally, color pixels in disocclusion holes are predicted using adjacent background pixels as predictors, and prediction residuals in a local neighborhood are coded using Graph Fourier Transform (GFT). Experimental results show that GBR-plus outperforms previous GBR, and has comparable performance as HEVC at mid to high bitrates with lower encoder complexity.


Published in:
Proceedings of IEEE ICASSP
Presented at:
IEEE ICASSP
Year:
2016
Publisher:
New York, Ieee
ISBN:
978-1-4799-9988-0
Keywords:
Laboratories:




 Record created 2016-01-11, last modified 2018-03-17


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