Analytical model for camera distance related 3D virtual view distortion estimation

We propose an analytical model to estimate the depth-error-induced synthesis distortion in 3D video, taking into account the configuration of the cameras. In particular, the model mathematically relates the Distance between camera positions (reference view and virtual view) to the Virtual View Distortion (VVD), thus it is denoted as DVVD model. Specifically, the DVVD model accounts for two modules: distribution of disparity errors and shift-induced distortion. The former one is derived under a Laplacian distribution assumption of depth errors, and the latter one is estimated under a Quadratic model. We further propose a linear Steady-State model by performing Taylor series approximation of the DVVD model over a region of practical interest. Experiment results demonstrate that both the DVVD and Steady-State models are capable of estimating the relationship between VVD and the distance between virtual/reference view. Therefore, our model can effectively inform camera setup for capturing, in particular, the setup of the cameras in situation where depth information will be compressed subsequently.