Range image quality assessment by Structural Similarity

We propose a new quality metric for range images that is based on the multi-scale structural similarity (MS-SSIM) index. The new metric operates in a manner to SSIM but allows for special handling of missing data. We demonstrate its utility by reevaluating the set of stereo algorithms evaluated in the Middlebury stereo vision page http://vision.middlebury.edu/stereo/. The new algorithm which we term Range SSIM (R-SSIM) index possesses features that make it an attractive choice for assessing the quality of range images.