ColorVideoVDP - how low-level human vision models help to design better quality metrics

The dominant image and video quality metrics rarely consider the physical specification of video content (luminance, frame rate, resolution) or incorporate models of human vision. In this talk, I will show how a robust video quality metric can be built from fundamental models of low-level human vision and how such a metric can generalize to any display specification (size, resolution, dynamic range) and viewing conditions (ambient light, viewing distance). I will explain how we built a model of contrast sensitivity for achromatic and chromatic spatiotemporal patterns (castleCSF) and combined it with a contrast masking model to create our new video quality metric - ColorVideoVDP. ColorVideoVDP brings many advantages, such as explainability, robustness to unseen distortions, differentiable formulation, and the ability to adapt to the physical characteristics of the content (frame rate, HDR/SDR colour spaces).