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A depth image provides partial geometric information of a 3D scene, namelythe shapes of physical objects as observed from a particular viewpoint. Thisinformation is important when synthesizing images of different virtual cameraviewpoints via depth-image-based rendering (DIBR). It has been shown that depthimages can be efficiently coded using contour-adaptive codecs that preserveedge sharpness, resulting in visually pleasing DIBR-synthesized images.However, contours are typically losslessly coded as side information (SI),which is expensive if the object shapes are complex.  In this paper, we pursue a new paradigm in depth image coding forcolor-plus-depth representation of a 3D scene: we pro-actively simplify objectshapes in a depth and color image pair to reduce depth coding cost, at apenalty of a slight increase in synthesized view distortion. Specifically, wefirst mathematically derive a distortion upper-bound proxy for 3DSwIM---aquality metric tailored for DIBR-synthesized images. This proxy reducesinterdependency among pixel rows in a block to ease optimization. We thenapproximate object contours via a dynamic programming (DP) algorithm tooptimally trade off coding cost of contours using arithmetic edge coding (AEC)with our proposed view synthesis distortion proxy. We modify the depth andcolor images according to the approximated object contours in an inter-viewconsistent manner. These are then coded respectively using a contour-adaptiveimage codec based on graph Fourier transform (GFT) for edge preservation andHEVC intra. Experimental results show that by maintaining sharp but simplifiedobject contours during contour-adaptive coding, for the same visual quality ofDIBR-synthesized virtual views, our proposal can reduce depth image coding rateby up to 22% compared to alternative coding strategies such as HEVC intra.

Object Shape Approximation & Contour Adaptive Depth Image Coding for Virtual View Synthesis