CHUG: Crowdsourced User-Generated HDR Video Quality Dataset

Sample frames from the CHUG dataset, showcasing diverse real-world UGC-HDR content with variations in lighting, motion, orientation, and distortions. Best viewed when zoomed in

How to Access the Dataset

Visit our GitHub repository to access metadata files. (We recommend visiting Github repository or Supplementary material for detailed instruction)
Download video IDs from chug-video.txt.
Use AWS CLI to download single videos (replace VIDEO_ID with with actual video ID):
```
aws s3 cp s3://ugchdrmturk/videos/VIDEO_ID.mp4 ./CHUG_Videos/
```

Use AWS CLI to download all videos:

 cat chug-video.txt | while read video; do
          aws s3 cp s3://ugchdrmturk/videos/${video}.mp4 ./CHUG_Videos/
      done

To play videos directly, replace VIDEO_ID in this URL:
https://ugchdrmturk.s3.us-east-2.amazonaws.com/videos/VIDEO_ID.mp4

https://ugchdrmturk.s3.us-east-2.amazonaws.com/videos/9ae245a27cc5ea9d2f3fae9692250281.mp4

Abstract

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.