Color VDP: A Visual Difference Predictor for Image, Video and Display Distortions

Assessing the visual quality of color images and videos is a key focus in fields such as computer graphics and computer vision. However, it is fundamentally challenging to develop reliable metrics for perceptual differences, as color appearance depends on spatial and temporal contexts in complex ways. In this webinar hosted by the Color Technical Group, Rafał Mantiuk will present his recent work on ColorVideoVDP, a quality metric for video and images that models how the human visual system processes luminance and color over space and time.

This metric is built on psychophysical models of chromatic spatiotemporal contrast sensitivity and cross-channel contrast masking, and it considers viewing conditions along with the geometric and photometric properties of displays. ColorVideoVDP supports various applications requiring spatiotemporal assessment of luminance and color distortions, including video streaming, display design, visual comparisons, and perceptually guided quality optimization. This webinar will be especially relevant for researchers studying low-level processing in human color vision and for those using these metrics to evaluate the quality of displayed content.

What You Will Learn:
• The challenges and approaches to developing quality metrics for color images and videos
• How to model spatial and temporal processing in the human visual system effectively
• How researchers and industry professionals can use this metric to assess the visual quality of displayed content

Who Should Attend:
• Academic and industry researchers interested in perceptual quality assessment metrics
• Academic and industry researchers interested in learning about low-level processing in the human color vision system

About the Presenter: Rafał K. Manituk from University of Cambridge

Rafał K. Mantiuk is a Professor of Graphics and Displays at the Department of Computer Science and Technology, University of Cambridge (UK). He received 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 the capture and display system that passed the visual Turing test - 3D objects were reproduced with fidelity that made them undistinguishable from their real counterparts. Further details: http://www.cl.cam.ac.uk/~rkm