Structural similarity-based synthesized view distortion estimation for depth map coding

Depth map coding aims to maximize the perceived visual quality of the synthesized virtual views instead of the depth maps. Therefore, it is necessary to develop new synthesized view distortion model to capture the effect of depth map distortion on the final quality of the synthesized views. This paper proposes a structural similarity-based synthesized view distortion (SS-SVD) model to relate perceptual distortion in coded depth map and synthesized view by deriving the relationship between the depth map distortion, warping error and synthesized view distortion. The SS-SVD model is applied to the rate distortion optimization which describes the relationship between depth coding bitrate and synthesized view distortion for depth map coding mode selection. Experimental results show that the proposed SS-SVD method obtains both better rate distortion performance and perceptual quality of synthesized views than JM reference software.