mlsanityData to 14 Oct 2026

Subjective Assessment of High Dynamic Range Videos Under Different Ambient Conditions

arXiv 2209.10005 ↗First posted 20 Sep 2022eess.IV cs.CVcited by 13

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Abstract (arXiv)

High Dynamic Range (HDR) videos can represent a much greater range of brightness and color than Standard Dynamic Range (SDR) videos and are rapidly becoming an industry standard. HDR videos have more challenging capture, transmission, and display requirements than legacy SDR videos. With their greater bit depth, advanced electro-optical transfer functions, and wider color gamuts, comes the need for video quality algorithms that are specifically designed to predict the quality of HDR videos. Towards this end, we present the first publicly released large-scale subjective study of HDR videos. We study the effect of distortions such as compression and aliasing on the quality of HDR videos. We also study the effect of ambient illumination on perceptual quality of HDR videos by conducting the study in both a dark lab environment and a brighter living-room environment. A total of 66 subjects participated in the study and more than 20,000 opinion scores were collected, which makes this the largest in-lab study of HDR video quality ever. We anticipate that the dataset will be a valuable resource for researchers to develop better models of perceptual quality for HDR videos.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 5 of its 20 references that are in the map. First posted on arXiv 20 Sep 2022.

Builds on

5 of 20 references.

References · 5

  1. 2020BBAND Index: A No-Reference Banding Artifact Predictorcited by 19
  2. 2020A Simple Model for Subject Behavior in Subjective Experimentscited by 15
  3. 2020Perceptual Video Quality Prediction Emphasizing Chroma Distortionscited by 10
  4. 2018Evaluating the Performance of Existing Full-Reference Quality Metrics on High Dynamic Range (HDR) Video Contentcited by 5
  5. 2021Assessment of Subjective and Objective Quality of Live Streaming Sports Videoscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 3; 2025: 2; 2026: 2.

13 papers.

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