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.

本研究引入了一种新的特征集，称为 HDRMAX 特征，可用于视频质量评估（VQA）算法，对于 SDR 视频可能无法充分考虑的 HDR 视频的失真进行敏感化处理。 这些特征可以修改自然视频统计（NVS）模型的强先验，从而捕捉通常由现有 VQA 模型难以考虑到的视觉影响最明显最亮和最暗的局部视频部分的失真，并在 HDR 视频上取得了显著的效果提升。