Depth Map Estimation based on Maximum a Posteriori Probability

Abstract

In this paper, we consider depth map estimation expressed as an optimization problem. We focus on the fitness function, for which we present a theoretical derivation based on a maximum a posteriori probability (MAP) rule. This is then used to show relations of interest with commonly used similarity metrics: sum of absolute differences (SAD) and sum of squared differences (SSD). The original derivations are also used to propose a depth estimation method. The experimental results are obtained with an implementation made on the basis of Moving Picture Expert Group (MPEG) Depth Estimation Reference Software (DERS). We show that with the proposed approach, it is possible to estimate a depth that allows higher quality of view synthesis (up to 2.8 dB of PSNR – Peak Signal-to-Noise Ratio) versus the original unsupervised DERS, when sub-optimal control parameters are used. If the DERS control parameters are optimized manually, the attained gain is smaller (up to 0.08 dB PSNR) but still does not need manual selection of control parameters.