Dongchang Li | Applied Math, University of Waterloo
A Novel Class of Metrics for Image Functions Designed to Accommodate Weber’s Model of Perception
Even though the usual L2 image fidelity measurements, such as MSE and PSNR, characterize the mean error for each pixel of the images, these traditional measurements are not designed to predict human visual perception of image quality. In other words, the standard L2-type optimization in the context of best approximation theory is not in accordance with human visual system. To provide alternative methods of measuring image distortion perceptually, we will discuss how to construct a novel class of metrics via intensity-based measures, which accommodate a well-known psychological model, Weber's model of perception, by allowing greater deviations at higher intensity values and lower deviations at lower intensity values. Meanwhile, we will consider the Weberized metrics as the optimization criteria and find the associated best Weber-based approximations.