---
license: cc-by-sa-4.0
language:
- en
tags:
- HDR
- video
- video-quality-assessment
- VQA
pretty_name: chug
size_categories:
- 1K<n<10K
---

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

### **2️⃣ Downloading Videos Using AWS CLI**
To download all videos:
```sh
cat chug-video.txt | while read video; do
    aws s3 cp s3://ugchdrmturk/videos/${video}.mp4 ./CHUG_Videos/
done
```

To download a single video:
```sh
aws s3 cp s3://ugchdrmturk/videos/VIDEO.mp4 ./CHUG_Dataset/
```

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

```bibtex
@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.