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Insights into how humans perceive and react to their visual surroundings have driven advancements in computer graphics, improving the efficiency and fidelity of display and rendering technologies. Computational models that capture the capabilities, limitations, and nuances of human vision have revealed numerous optimization strategies that enhance system performance without perceptible degradation in user experience. The emergence of applications with complex computational capabilities and human interaction-aware technologies---such as XR, assisted driving, video games, and esports---has not only introduced new opportunities for optimizing graphics but also for enhancing human performance, productivity, and safety beyond conventional limits.
In this PhD dissertation, we investigate various aspects of the human visual system and develop computational models and algorithms that complement perceptual and behavioral constraints to enhance user experience. We explore topics such as leveraging color encoding limitations to optimize display output, identifying and correcting inaccuracies in motion perception, and measuring human decision-making and motor control latency to assess the temporal effects of displayed imagery.
Through this work, we demonstrate how psychophysical methodologies, originally designed to study human perception and behavior, can be applied to understanding human-computer joint systems. By addressing inefficiencies, bottlenecks, and inaccuracies within this system, we show how computers can be improved to reduce power consumption, computation, and bandwidth, while human users can be enhanced in speed and accuracy.