  ColorVideoVDP is a video and image quality metric that models spatial and
temporal aspects of vision, for both luminance and color. The metric is built
on novel psychophysical models of chromatic spatiotemporal contrast sensitivity
and cross-channel contrast masking. It accounts for the viewing conditions,
geometric, and photometric characteristics of the display. It was trained to
predict common video streaming distortions (e.g. video compression, rescaling,
and transmission errors), and also 8 new distortion types related to AR/VR
displays (e.g. light source and waveguide non-uniformities). To address the
latter application, we collected our novel XR-Display-Artifact-Video quality
dataset (XR-DAVID), comprised of 336 distorted videos. Extensive testing on
XR-DAVID, as well as several datasets from the literature, indicate a
significant gain in prediction performance compared to existing metrics.
ColorVideoVDP opens the doors to many novel applications which require the
joint automated spatiotemporal assessment of luminance and color distortions,
including video streaming, display specification and design, visual comparison
of results, and perceptually-guided quality optimization.
