In recent years a number of different vision models have been proposed to assist in the evaluation of image quality. However, there have been few attempts to independently evaluate these models and to make comparisons between them. In this paper we first summarize the work that has been done in image quality modeling. We then select two of the leading image quality models, the Daly Visible Differences Predictor and the Sarnoff Visual Discrimination Model, for further study. We begin by describing our implementation, which was done from the published papers, of each of the models. We next discuss the similarities and the differences between the two models. The paper ends with a summary of the important advantages of each approach. The comparison of these two models is presented in the context of our research interests which are image quality evaluation for both computer imaging and computer graphics tasks. The paper includes illustrations drawn from these two areas.

Many real-world scenes contain a dynamic range that exceeds conventional display technology by several orders of magnitude. Through the combination of several existing technologies, new high dynamic range displays, capable of reproducing a range of intensities much closer to that of real environments, have been constructed. These benefits come at the cost of more optically complex devices; involving two image modulators, controlled in unison, to display images. We present several methods of rendering images to this new class of devices for reproducing photometrically accurate images. We discuss the process of calibrating a display, matching the response of the device with our ideal model. We then derive series of methods for efficiently displaying images, optimized for different criteria and evaluate them in a perceptual framework.