Making Video Quality Assessment Models Robust to Bit Depth
This addresses the issue of video quality assessment for HDR content, which is important for video processing and streaming applications, but it is incremental as it enhances existing VQA models rather than creating a new paradigm.
The paper tackled the problem of Video Quality Assessment (VQA) models performing poorly on High Dynamic Range (HDR) and 10-bit videos by introducing HDRMAX features, which improved state-of-the-art VQA models' performance on HDR and 10-bit distorted videos.
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.