Algorithm for automated eye-strain reduction in real stereoscopic images and sequences

Eye strain is often experienced when viewing a stereoscopic image pair on a flat display device (e.g., a computer monitor). Violations of two relationships that contribute to this eye strain are: 1) the accommodation/convergence breakdown and 2) the conflict between interposition and disparity depth cues. We describe a simple algorithm that reduces eye strain through horizontal image translation and corresponding image cropping, based on a statistical description of the estimated disparity within a stereoscopic image pair. The desired amount of translation is based on the given stereoscopic image pair, and, therefore, requires no user intervention. In this paper, we first develop a statistical model of the estimated disparity that incorporates the possibility of erroneous estimates. An estimate of the actual disparity range is obtained by thresholding the disparity histogram to avoid the contribution of false disparity values. Based on the estimated disparity range, the image pair is translated to force all points to lie on, or behind, the screen surface. This algorithm has been applied to diverse real stereoscopic images and sequences. Stereoscopic image pairs, which were often characterized as producing eye strain and confusion, produced comfortable stereoscopy after the automated translation.