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Statistical Model of Color and Disparity with Application to Bayesian Stereopsis Che-Chun Su1, Alan C. Bovik2, and Lawrence K. Cormack3 Department of Electrical and Computer Engineering1,2, Department of Psychology3 The University of Texas at Austin Austin, Texas chechunsu@1, bovik@2, cormack@3 Abstract—Extensive research has been conducted relating the natural range data. Then, the analysis and derivation of statistical mod- scene statistics of luminance and depth; however, very little work has els of the marginal and conditional distributions between lumi- been done on analyzing the statistical relationships between depth and nance/chrominance and disparity are included in Section III. Next, chromatic information. In this paper, we examine and derive statistical models between disparity and both luminance and chrominance informa- Section IV explains how to apply the derived statistical models of lu- tion by transforming natural images into the more perceptually relevant minance/chrominance and disparity to the Bayesian stereo algorithm, CIELAB color space. To demonstrate the effectiveness of these models, followed by simulation results in Section V. Finally, Section VI gives we further exploit them with application to Bayesian stereo algorithms. the conclusion. The simulation results show that incorporating the derived statistical models augments the performance of Bayesian stereo algorithms. In ad- II. DATA ACQUISITION AND PRE-PROCESSING dition, these results also support psychophysical evidence that chromatic information can improve binocular visual processing. We obtained a dataset of 2D color images and co-regist