Statistical model of color and disparity with application to Bayesian stereopsis

Extensive research has been conducted relating the natural scene statistics of luminance and depth; however, very little work has been done on analyzing the statistical relationships between depth and chromatic information. In this paper, we examine and derive statistical models between disparity and both luminance and chrominance information by transforming natural images into the more perceptually relevant CIELAB color space. To demonstrate the effectiveness of these models, we further exploit them with application to Bayesian stereo algorithms. The simulation results show that incorporating the derived statistical models augments the performance of Bayesian stereo algorithms. In addition, these results also support psychophysical evidence that chromatic information can improve binocular visual processing.