Color Discrimination and Chromatic Adaptation: Measuring, Modeling, and Exploiting Them for Energy-Efficient XR Displays

As virtual and extended reality (XR) technologies continue to evolve and become more accessible to everyday consumers, there is an increasing need to characterize color vision across the entire visual field in greater detail. In particular, it is becoming increasingly important to revisit foundational visual functions, such as chromatic adaptation, to examine how they operate in XR environments, which may differ significantly from traditional laboratory environments. These efforts not only contribute to the refinement of XR systems but also have the potential to advance our fundamental understanding of human vision.

In this webinar, Prof. Yuhao Zhu will explore the science of color discrimination and how it changes with visual eccentricity, as well as the temporal dynamics of chromatic adaptation under dynamic illumination. He will also introduce a series of psychophysical paradigms developed to measure and model these phenomena, and explain how these models can be used to optimize power efficiency in XR systems that utilize organic light-emitting diode (OLED) displays. By attending this session, you will gain insights that bridge theoretical research, experimental methods, and practical application, providing valuable knowledge for both academic and industry professionals.

What You Will Learn:
• Fundamentals of color discrimination and chromatic adaptation
• Methods to measure and model these visual processes
• Applications of color vision research to improve XR system performance

Who Should Attend:
• Those interested in the fundamentals of human color vision
• Those interested in applying foundational knowledge of color vision to technologies such as extended reality (XR)

About the Presenter: Prof. Yuhao Zhu from University of Rochester

Yuhao Zhu is an Associate Professor of Computer Science, Brain and Cognitive Sciences, and an affiliated member of Center for Visual Science, all at University of Rochester. Along with his students, he is mostly drawn to research that spans imaging (light transport, optics, and image sensors), human visual perception (psychophysics and computational modeling), and computer systems (computer architecture and programming systems). More about his research can be found at: http://www.horizon-lab.org/.