Improvement of stereo disparity estimation through balanced filtering: the sliding-block approach

In a typical disparity (or motion) estimation algorithm developed for inter-image prediction, an interpolation of intensities is applied to one of the two images used. Therefore, non-filtered intensities of the image being predicted are compared with lowpass-filtered intensities of the other image of the stereo pair. Consequently, noise and detail suppression in the two images are unequal. In this paper we propose to apply the same (balanced) filtering to both images. In addition to image smoothing that helps avoid unreliable intensity matches, a low-pass filter is used to carry out intensity interpolation at the same time; the computation of sub-pixel attributes is consistent with lowpass filtering of both images unlike arbitrary linear or cubic interpolation applied to one image only. The proposed approach lends itself naturally to a multiresolution implementation. We apply the new approach to stereo disparity estimation based on sliding blocks. Using synthetic and natural data we experimentally compare the new approach with the traditional sliding-block method. For standard stereoscopic images we demonstrate up to 2.4dB reduction of disparity-compensated prediction error over the traditional sliding-block method.