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Original and reproduced art are usually viewed under quite different viewing conditions. One of the interesting differences in viewing condition is size difference. The main focus of this research was investigation of the effect of image size on color perception of rendered images. This research had several goals. The first goal was to develop an experimental paradigm for measuring the effect of image size on color appearance. The second goal was to identify the most affected image attributes for changes of image size. The final goal was to design and evaluate algorithms to compensate for the change of visual angle (size). To achieve the first goal, an exploratory experiment was performed using a colorimetrically characterized digital projector and LCD. The projector and LCD were light emitting devices and in this sense were similar soft-copy media. The physical sizes of the reproduced images on the LCD and projector screen could be very different. Additionally, one could benefit from flexibility of soft-copy reproduction devices such as real-time image rendering, which is essential for adjustment experiments. The capability of the experimental paradigm in revealing the change of appearance for a change of visual angle (size) was demonstrated by conducting a paired-comparison experiment. Through contrast matching experiments, achromatic and chromatic contrast and mean luminance of an image were identified as the most affected attributes for changes of image size. Measurement of the extent and trend of changes for each attribute were measured using matching experiments. Proper algorithms to compensate for the image size effect were design and evaluated. The correction algorithms were tested versus traditional colorimetric image rendering using a paired-comparison technique. The paired-comparison results confirmed superiority of the algorithms over the traditional colorimetric image rendering for the size effect compensation.

The advent of computers with high processing power has led to the generation of large, multidimensional collections of data with increasing size and dimensionality. This has led to a critical need for ways to manage, explore and analyze large, multidimensional information spaces. Visualization lends itself well to the challenge of exploring and analyzing these datasets by managing and presenting information in a visual form to facilitate rapid, effective, and meaningful analysis of data by harnessing the strengths of the human visual system. Most visualization techniques are based on the assumption that the display device has sufficient resolution, and that our visual acuity is adequate for completing the analysis tasks. However, this may not be true, particularly for specialized display devices (e.g., PDAs or large-format projection walls). Our goal is to: (1) determine the amount of information a particular display environment can encode; (2) design visualizations that maximize the information they represent relative to this upper-limit; and (3) dynamically update a visualization when the display environment changes to continue to maintain high levels of information content. A collection of controlled psychophysical experiments were designed, executed, and analyzed to identify thresholds for display resolution and visual acuity for four visual features: hue, luminance, size, and orientation. In computer graphics, level-of-detail hierarchies are often used to reduce geometric complexity in situations where full resolution models are unnecessary. Using results from our experiments, we applied similar logic to a visualization environment. If certain properties of a dataset cannot be seen because their current visual representation falls below a resolution or acuity threshold, they need not be included in the visualization. We built level-of-detail perceptual display hierarchies to automatically add or remove information from a visualization as viewer's perspective on the data changes, or as the data is visualized across different display devices. Our display hierarchies are combined with existing rules on the use of perception in visualization to improve our ability to construct visualizations that are perceptually optimal for a particular dataset, analysis tasks, and viewing conditions. We conclude by visualizing multidimensional weather data using a prototype visualization system that applies our perceptual display hierarchy rules.

In recent times there has been an introduction of new and more retroreflective sheeting materials for traffic sign legends and backgrounds. It has been suspected that higher brightness may actually have a detrimental effect on legibility performance when reading traffic signs under automobile headlight illumination at night. The objective of this study is to develop a legibility model and modify a PC software to predict the legibility of text on traffic signs for high luminance and contrast conditions for near minimum size threshold conditions. The legibility model is based on experimental results from a previous study conducted at the ORITE Human Factors and Ergonomics Laboratory at Ohio University, which investigated the nighttime legibility of traffic signs as a function of legend and background luminance. Two functions were derived which when applied to the legend luminance for positive contrast or the background luminance for negative contrast enables us to predict fairly accurately the probability of correct identification for Landolt rings presented in the dark near threshold (visual angle)conditions.