We present a no-reference video quality model and algorithm that delivers
standout performance for High Dynamic Range (HDR) videos, which we call
HDR-ChipQA. HDR videos represent wider ranges of luminances, details, and
colors than Standard Dynamic Range (SDR) videos. The growing adoption of HDR in
massively scaled video networks has driven the need for video quality
assessment (VQA) algorithms that better account for distortions on HDR content.
In particular, standard VQA models may fail to capture conspicuous distortions
at the extreme ends of the dynamic range, because the features that drive them
may be dominated by distortions {that pervade the mid-ranges of the signal}. We
introduce a new approach whereby a local expansive nonlinearity emphasizes
distortions occurring at the higher and lower ends of the {local} luma range,
allowing for the definition of additional quality-aware features that are
computed along a separate path. These features are not HDR-specific, and also
improve VQA on SDR video contents, albeit to a reduced degree. We show that
this preprocessing step significantly boosts the power of distortion-sensitive
natural video statistics (NVS) features when used to predict the quality of HDR
content. In similar manner, we separately compute novel wide-gamut color
features using the same nonlinear processing steps. We have found that our
model significantly outperforms SDR VQA algorithms on the only publicly
available, comprehensive HDR database, while also attaining state-of-the-art
performance on SDR content.

本文提出了一种无参考视频质量模型和算法，它可以为广泛采用的 HDR 视频网络提供更好的视频质量评估，在 HDR 和 SDR 内容中均表现出优异的性能。