Probabilistic reliability based view synthesis for FTV

Abstract

View synthesis using depth maps is an important application in 3D image processing. In this paper, a novel method is proposed for the plausible view synthesis of Free-viewpoint TV (FTV), using two input images and their depth maps. The depth estimation based on stereo matching is known to be error-prone, leading to noticeable artifacts in the synthesized new views. To produce high-quality view synthesis, we introduce a probabilistic framework which constrains the reliability of each pixel of new view by Maximizing Likelihood (ML). The spatial adaptive reliability is provided by incorporating Gamma hyper-prior and the synthesis error approximation. Furthermore, we generate the virtual view by solving a Maximum a Posterior (MAP) problem using graph cuts. We compare the proposed method with other depth based view synthesis approaches on MPEG test sequences. The results show the outperformance of our method both at subjective artifacts reduction and objective PSNR improvement.