An evaluation of stereo matching methods for view interpolation

Abstract

Stereo matching has a long history in image processing and computer vision. In fact, there are inumerous approaches reported in the literature, and quantitative evaluation is usually performed by comparing the obtained disparity maps with ground truth data (using the MSE, for instance). One important application of stereo matching is view interpolation, where it is desired to produce a new synthetic view from (at least) a pair of images and the corresponding disparity maps. In view interpolation, evaluation is mostly qualitative (visual quality of the synthesized image), and quantitative approaches compute objective similarity metrics between the synthesized image and the actual image at the same position (e.g. PSNR). The main goal of this paper is to evaluate the impact of several different stereo matching algorithms in a view interpolation context, relating the quality of the disparity maps with the quality of the corresponding synthesized views using standardized datasets. In this paper, experiments using the MPEG reference software for view interpolation and more than twenty datasets are presented and discussed. Our results indicate that the use of the common percentage of bad pixels as a metric for stereo matching methods does not translate well to the quality of view interpolation.