# Subjective Assessment of High Dynamic Range Videos Under Different Ambient Conditions

arXiv: https://arxiv.org/abs/2209.10005 · HTML: https://mlsanity.com/paper/2209.10005/

## 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 (most cited first, 5 of 5)

- [BBAND Index: A No-Reference Banding Artifact Predictor](https://mlsanity.com/paper/2002.11891/) (2020)
- [A Simple Model for Subject Behavior in Subjective Experiments](https://mlsanity.com/paper/2004.02067/) (2020)
- [Perceptual Video Quality Prediction Emphasizing Chroma Distortions](https://mlsanity.com/paper/2009.11203/) (2020)
- [Evaluating the Performance of Existing Full-Reference Quality Metrics on High Dynamic Range (HDR) Video Content](https://mlsanity.com/paper/1803.04815/) (2018)
- [Assessment of Subjective and Objective Quality of Live Streaming Sports Videos](https://mlsanity.com/paper/2106.08431/) (2021)

## Built on by (most cited first, 13 of 13)

- [HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content](https://mlsanity.com/paper/2304.13625/) (2023)
- [ColorVideoVDP: A visual difference predictor for image, video and display distortions](https://mlsanity.com/paper/2401.11485/) (2024)
- [Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models](https://mlsanity.com/paper/2303.13031/) (2023)
- [HDR or SDR? A Subjective and Objective Study of Scaled and Compressed Videos](https://mlsanity.com/paper/2304.13162/) (2023)
- [HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment](https://mlsanity.com/paper/2311.11059/) (2023)
- [Making Video Quality Assessment Models Robust to Bit Depth](https://mlsanity.com/paper/2304.13092/) (2023)
- [HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos](https://mlsanity.com/paper/2304.13156/) (2023)
- [Subjective Quality Assessment of Compressed Tone-Mapped High Dynamic Range Videos](https://mlsanity.com/paper/2403.15061/) (2024)
- [CHUG: Crowdsourced User-Generated HDR Video Quality Dataset](https://mlsanity.com/paper/2510.09879/) (2025)
- [Encoder-Quantization-Motion-based Video Quality Metrics](https://mlsanity.com/paper/2404.06620/) (2024)
- [Towards Unified Video Quality Assessment](https://mlsanity.com/paper/2512.02224/) (2025)
- [Learning Flexible Generalization in Video Quality Assessment by Bringing Device and Viewing Condition Distributions](https://mlsanity.com/paper/2607.04643/) (2026)
- [Neural video codecs quality assessment dataset and benchmark](https://mlsanity.com/paper/2608.29331/) (2026)

## 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.