Bio:

Rafał K. Mantiuk is a Professor of Graphics and Displays at the Department of Computer Science and Technology, University of Cambridge (UK). He received a Ph.D. from the Max-Planck Institute for Computer Science (Germany). His recent interests focus on computational displays, rendering, and imaging algorithms that adapt to human visual performance and deliver the best image quality given limited resources, such as computation time or bandwidth. He contributed to early work on high-dynamic-range imaging, including quality metrics (HDR-VDP), video compression, and tone mapping. More recently, he led an ERC-funded project on a capture-and-display system that passed the visual Turing test: 3D objects were reproduced with sufficient fidelity to be indistinguishable from their real counterparts. Further details: http://www.cl.cam.ac.uk/~rkm38/.

Abstract:

As multimedia experiences expand beyond conventional video streaming into AR/XR, automotive displays, immersive telepresence, and generative graphics, quality metrics must evolve to remain relevant. This talk argues that the next generation of quality assessment should be built on three pillars: psychophysics, optimisation, and graphics. First, psychophysical models and data provide the foundation for metrics that reflect how humans actually perceive distortions, rather than how signals differ in a purely mathematical sense. A perceptually grounded metric must account for the visual system’s nonlinearities, attentional limits, and sensitivity to display characteristics such as resolution, luminance, contrast, and viewing geometry. Second, metrics must be evaluated not just on benchmark datasets but as objectives in real optimisation problems, where their loss landscapes reveal whether they are suitable for parameter tuning and system design. Third, computer graphics introduces a rapidly growing class of media and artifacts, from rendered imagery and neural representations to mixed-reality content, that differs substantially from traditional compression and transmission distortions. Metrics must therefore be robust to new artifact types and generalize beyond the datasets on which they were trained or validated. Together, these three pillars suggest a broader agenda for future media quality: metrics should be human-aware, optimisation-ready, and robust to the visual diversity of emerging graphics-driven experiences

Bio:

Prof Sylvia Pan is a Professor of Virtual Reality at Goldsmiths, University of London. She co-leads the SeeVR research lab including 10 academics and researchers. She holds a PhD in Virtual Reality, and an MSc in Computer Graphics, both from UCL, and a BEng in Computer Science from Beihang University. Her research interest is the use of Virtual Reality as a medium for real-time social interaction, in particular in the application areas of medical training and therapy. Her work has been featured multiple times in the media, including BBC Horizon, the New Scientist magazine, and the Wall Street Journal. Her 2017 Coursera VR specialisation attracted over 100,000 learners globally, and she co-leads on the MA/MSc in Virtual and Augmented Reality at Goldsmiths Computing.

Abstract: