  We introduce a novel feature set, which we call HDRMAX features, that when
included into Video Quality Assessment (VQA) algorithms designed for Standard
Dynamic Range (SDR) videos, sensitizes them to distortions of High Dynamic
Range (HDR) videos that are inadequately accounted for by these algorithms.
While these features are not specific to HDR, and also augment the equality
prediction performances of VQA models on SDR content, they are especially
effective on HDR. HDRMAX features modify powerful priors drawn from Natural
Video Statistics (NVS) models by enhancing their measurability where they
visually impact the brightest and darkest local portions of videos, thereby
capturing distortions that are often poorly accounted for by existing VQA
models. As a demonstration of the efficacy of our approach, we show that, while
current state-of-the-art VQA models perform poorly on 10-bit HDR databases,
their performances are greatly improved by the inclusion of HDRMAX features
when tested on HDR and 10-bit distorted videos.
