Disparity Estimation using Belief Propagation for View Interpolation

In this paper, we propose a modified disparity estimation method using belief propagation (BP) for view interpolation. The view interpolation method generates an arbitrary intermediate view image with disparity. To extract disparities, we modified the BP based stereo matching algorith which solves the problem using Bayesian belief propagation. We propose two methods to improve accuracy of disparity. The first is about the matching cost computation. Instead of using pixel differences between corresponding pixels, we use block cost computation. The second is quad-tree region dividing for disparity refinement. Since narrow and accurate search range can guarantee accuracy of estimation, we divide the image into four distinct regions and refine disparities. By experiments, we improved quality of the interpolated images over 1~4 dB on average.