# CHUG: Crowdsourced User-Generated HDR Video Quality Dataset

arXiv: https://arxiv.org/abs/2510.09879 · HTML: https://mlsanity.com/paper/2510.09879/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [KVQ: Kwai Video Quality Assessment for Short-form Videos](https://mlsanity.com/paper/2402.07220/) (2024)
- [A Simple Model for Subject Behavior in Subjective Experiments](https://mlsanity.com/paper/2004.02067/) (2020)
- [Subjective Assessment of High Dynamic Range Videos Under Different Ambient Conditions](https://mlsanity.com/paper/2209.10005/) (2022)
- [YouTube SFV+HDR Quality Dataset](https://mlsanity.com/paper/2406.05305/) (2024)
- [HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment](https://mlsanity.com/paper/2311.11059/) (2023)
- [HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos](https://mlsanity.com/paper/2304.13156/) (2023)

## Built on by (most cited first, 3 of 3)

- [Seeing Beyond 8bits: Subjective and Objective Quality Assessment of HDR-UGC Videos](https://mlsanity.com/paper/2603.00938/) (2026)
- [LumaFlux: Lifting 8-Bit Worlds to HDR Reality with Physically-Guided Diffusion Transformers](https://mlsanity.com/paper/2604.02787/) (2026)
- [LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models](https://mlsanity.com/paper/2607.26237/) (2026)

## Abstract (arXiv)

High Dynamic Range (HDR) videos enhance visual experiences with superior brightness, contrast, and color depth. The surge of User-Generated Content (UGC) on platforms like YouTube and TikTok introduces unique challenges for HDR video quality assessment (VQA) due to diverse capture conditions, editing artifacts, and compression distortions. Existing HDR-VQA datasets primarily focus on professionally generated content (PGC), leaving a gap in understanding real-world UGC-HDR degradations. To address this, we introduce CHUG: Crowdsourced User-Generated HDR Video Quality Dataset, the first large-scale subjective study on UGC-HDR quality. CHUG comprises 856 UGC-HDR source videos, transcoded across multiple resolutions and bitrates to simulate real-world scenarios, totaling 5,992 videos. A large-scale study via Amazon Mechanical Turk collected 211,848 perceptual ratings. CHUG provides a benchmark for analyzing UGC-specific distortions in HDR videos. We anticipate CHUG will advance No-Reference (NR) HDR-VQA research by offering a large-scale, diverse, and real-world UGC dataset. The dataset is publicly available at: https://shreshthsaini.github.io/CHUG/.