Image Statistics as Glossiness and Translucency Predictor in Photographs of Real-world Objects

Abstract

We interpret our surrounding based on the visual stimuli, and perceive objects and materials around us to have various attributes, like color, glossiness, and translucency. We analyze the three-dimensional world based on the two-dimensional images detected by our retina. The state-of-the-art works conclude that the human visual system has a poor ability to fully understand and invert the complex optical nature of light and matter interaction. Some authors rather propose that the human brain calculates image statistics to perceive appearance, demonstrating correlation between perceptual attributes and various statistical metrics. However, the illustrated examples are usually unrealistic nearly-perfect stimuli, making real-life robustness of the findings questionable. In this study, we analyzed image statistics of photos of real world objects, and assessed the performance of statistical image metrics proposedly used by the human visual system. We identified very interesting trends, as well as limitations.