A perceptually uniform gloss space was defined in Pellacini et al. [2000] as a reparameterization of the Ward BRDF model. Two dimensions, distinctness-of-image gloss and contrast gloss, were found to be enough to describe gloss perception, and the CIELAB lightness function was added to represent the diffuse component.

The overarching goal of this research was to compare different rendering solutions in order to understand why some yield better results specifically when applied to rendering synthetic objects into real photographs. A psychophysical experiment was conducted in which the composite images were judged for accuracy against the original photograph. In addition, iCAM, an image color appearance model was also used to calculate image differences for the same set of images. Conclusions obtained included the effect of global illumination on the accuracy of the final composite rendering. Also, it was discovered that the original rendering with all of its artifacts is not necessarily an indicator of the final composite image's judged accuracy. Finally, initial results show promise in using iCAM to predict a relationship similar to the psychophysics, which could eventually be used in-the-rendering-loop to achieve photorealism with minimized computation.

A series of three experiments has been performed to test both the preference and accuracy of high dynamic-range (HDR) rendering algorithms in digital photography application. The goal was to develop a methodology for testing a wide variety of previously published tone-mapping algorithms for overall preference and rendering accuracy. A number of algorithms were chosen and evaluated first in a paired-comparison experiment for overall image preference. A rating-scale experiment was then designed for further investigation of individual image attributes that make up overall image preference. This was designed to identify the correlations between image attributes and the overall preference results obtained from the first experiments. In a third experiment, three real-world scenes with a diversity of dynamic range and spatial configuration were designed and captured to evaluate seven HDR rendering algorithms for both of their preference and accuracy performance by comparing the appearance of the physical scenes and the corresponding tone-mapped images directly. In this series of experiments, a modified Durand and Dorsey's bilateral filter technique consistently performed well for both preference and accuracy, suggesting that it is a good candidate for a common algorithm that could be included in future HDR algorithm testing evaluations. The results of these experiments provide insight for understanding of perceptual HDR image rendering and should aid in design strategies for spatial processing and tone mapping. The results indicate ways to improve and design more robust rendering algorithms for general HDR scenes in the future. Moreover, the purpose of this research was not simply to find out the “best” algorithms, but rather to find a more general psychophysical experiment based methodology to evaluate HDR image-rendering algorithms. This paper provides an overview of the many issues involved in an experimental framework that can be used for these evaluations.

In this paper we develop a computational model of adaptation and spatial vision for realistic tone reproduction. The model is based on a multiscale representation of pattern, luminance, and color processing in the human visual system. We incorporate the model into a tone reproduction operator that maps the vast ranges of radiances found in real and synthetic scenes into the small fixed ranges available on conventional display devices such as CRT's and printers. The model allows the operator to address the two major problems in realistic tone reproduction: wide absolute range and high dynamic range scenes can be displayed; and the displayed images match our perceptions of the scenes at both threshold and suprathreshold levels to the degree possible given a particular display device. Although in this paper we apply our visual model to the tone reproduction problem, the model is general and can be usefully applied to image quality metrics, image compression methods, and perceptually-based...

Using a paired comparison paradigm, various gamut mapping algorithms were evaluated using simple rendered images and artificial gamut boundaries. The test images consisted of simple rendered spheres floating in front of a gray background. Using CIELAB as our device-independent color space, cut-off values for lightness and chroma, based on the statistics of the images, were chosen to reduce the gamuts for the test images. The gamut mapping algorithms consisted of combinations of clipping and mapping the original gamut in linear piecewise segments. Complete color space compression in RGB and CIELAB was also tested. Each of the colored originals (R,G,B,C,M,Y, and Skin) were mapped separately in lightness and chroma. In addition, each algorithm was implemented with saturation (C(*)/L(*)) allowed to vary or retain the same values as in the original image. Pairs of test images with reduced color gamuts were presented to twenty subjects along with the original image. For each pair the subjects chose the test image that better reproduced the original. Rank orders and interval scales of algorithm performance with confidence limits were then derived. Clipping all out-of-gamut colors was the best method for mapping chroma. For lightness mapping at low lightness levels and high lightness levels particular gamut mapping algorithms consistently produced images chosen as most like the original. The choice of device-independent color space may also influence which gamut mapping algorithms are best.

An environment was established to perform device-independent color reproduction of full-color pictorial images. In order to determine the required precision for this environment, an experiment was performed to psychophysically measure colorimetric tolerances for six images using paired comparison techniques. These images were manipulated using 10 linear and nonlinear functions in the CIELAB dimensions of lightness, chroma, and hue angle. Perceptibility tolerances were determined using probit analysis. From these results, the necessary precision in number of bits per color channel was determined for both the CIELAB and the CRT rgb device color spaces. For both the CIELAB color space and the CRT rgb device space, approximately eight color bits per channel were required for imperceptible color differences for pictorial images, and 10 bits per channel were required for computational precision.