3D Visual Activity Assessment Based on Natural Scene Statistics
One of the most challenging ongoing issues in the field of 3D visual research is how to perceptually quantify object and surface visualizations that are displayed within a virtual 3D space between a human eye and 3D display. To seek an effective method of quantification, it is necessary to measure various elements related to the perception of 3D objects at different depths. We propose a new framework for quantifying 3D visual information that we call 3D visual activity (3DVA), which utilizes natural scene statistics measured over 3D visual coordinates. We account for important aspects of 3D perception by carrying out a 3D coordinate transform reflecting the nonuniform sampling resolution of the eye and the process of stereoscopic fusion. The 3DVA utilizes the empirical distortions of wavelet coefficients to a parametric generalized Gaussian probability distribution model and a set of 3D perceptual weights. We conducted a series of simulations that demonstrate the effectiveness of the 3DVA for quantifying the statistical dynamics of visual 3D space with respect to disparity, motion, texture, and color. A successful example application is also provided, whereby 3DVA is applied to the problem of predicting visual fatigue experienced when viewing 3D displays.