shreshthsaini/CHUG

Webpage: https://live.ece.utexas.edu/research/chug/index.html

🔗 Accessing Videos

1️⃣ Directly from AWS S3 (via Browser)

Each video is hosted on AWS S3 and can be accessed using:

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

Replace VIDEO with a hashed video ID from chug.csv or chug-video.txt.

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

2️⃣ Downloading Videos Using 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 download a single video:

aws s3 cp s3://ugchdrmturk/videos/VIDEO.mp4 ./CHUG_Dataset/

To download selected videos, create a new text file with list of video IDs:

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

---

📊 Key Dataset Insights

• Higher resolutions & bitrates improve perceptual quality 📈
• UGC-HDR videos exhibit unique distortions, including banding and overexposure 🌈
• Landscape vs. Portrait orientation has minimal impact on MOS, though portrait is slightly favored 📱
• Compression artifacts degrade MOS significantly at low bitrates ⚠️

---

🏆 Use Cases and Future Impact

CHUG serves as a crucial benchmark for No-Reference UGC HDR Video Quality Assessment (NR-HDR-VQA) and real-world HDR streaming quality analysis. Key applications:

✅ UGC-HDR Distortion Analysis

• CHUG captures banding, overexposure, luminance inconsistencies, making it an essential dataset for HDR distortion research.

✅ HDR Streaming Optimization

• Streaming providers can leverage CHUG to evaluate bitrate-resolution trade-offs, improving HDR compression pipelines.

✅ Advancing HDR Quality Metrics

• CHUG enables refinement of HDR-specific VQA metrics such as HDR-VMAF, HDR-SSIM, and learning-based perceptual models.

CHUG is expected to guide industry standards and HDR-VQA research for years to come.

---

📜 Citation

If you use CHUG in your research, please cite us:

@INPROCEEDINGS{11084488,
  author={Saini, Shreshth and Bovik, Alan C. and Birkbeck, Neil and Wang, Yilin and Adsumilli, Balu},
  booktitle={2025 IEEE International Conference on Image Processing (ICIP)},
  title={CHUG: Crowdsourced User-Generated HDR Video Quality Dataset},
  year={2025},
  volume={},
  number={},
  pages={2504-2509},
  keywords={Visualization;Video on demand;User-generated content;Benchmark testing;Distortion;Quality assessment;High dynamic range;Web sites;Surges;Videos;Crowdsourced;High Dynamic Range (HDR);Video Quality Assessment;HDR VQA Dataset;User-Generated Content (UGC)},
  doi={10.1109/ICIP55913.2025.11084488}
}

---

📜 License

CHUG is released under a Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) License.