Perceptual Crosstalk Prediction on Autostereoscopic 3D Display

Perceptual crosstalk prediction for autostereoscopic 3D displays is of fundamental importance in determining the level of quality perceived by humans in terms of the display performance and the 3D viewing experience. However, no robust framework exists to quantify perceptual crosstalk while taking into account the hardware structure of a display as well as its content characteristics via content analysis. In this paper, we present a 3D perceptual crosstalk predictor (3D-PCP) that can be used to predict crosstalk in a unique way when viewing autostereoscopic 3D displays. 3D-PCP captures hardware features using an optical Fourier transform-light measurement device and content features through content analysis based on information theory. By deriving the disparity, luminance, color, and texture maps, this approach defines the visual entropy, mutual information, and relative entropy in order to investigate the influences of the 3D scene characteristics on perceptual crosstalk. The experimental results demonstrate that the 3D-PCP output is highly correlated with subjective scores.