A high-resolution NR-IQA database for high-quality authentic photos 

The dataset comprises 6073 UHD-1 (4k) images. These were selected from the top photos on Pixabay.com based on popularity indicators, out of a total of 1.2 million photos available at the time of indexing. The dataset ensures high photo aesthetics and technical quality, and excludes synthetic images, such as purely computer generated graphics, drawings or those that are heavily edited.

​​Features

• Resolution: all images are originally larger than UHD-1 (3840x2160 pixels) and are downscaled to a width of 3840px maintaining their aspect ratio.
• Authenticity: synthetic images were manually excluded to ensures all are genuine photos.
• Reliability: 10 thoroughly selected professional photographers and graphic artists rated each photo twice several days apart, leading to highly reliable mean opinion scores (MOS).

​Cite us

The UHD-IQA database is freely available to the research community. If you use our database in your research, please cite us:

@misc{hosu2024uhdiqa,
      title={UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality Assessment}, 
      author={Vlad Hosu and Lorenzo Agnolucci and Oliver Wiedemann and Daisuke Iso},
      year={2024},
      eprint={2406.17472},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2406.17472}, 
}

Image license
All images are available under the CC0 license. 
Images have been published to Pixabay.com prior to 2019.

Acknowledgements
The dataset has been prepared at the University of Konstanz, with funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 251654672 – TRR 161.

Browse images

The dataset supports the training and testing of models for the UHD-IQA challenge held at the ECCV AIM 2024 Workshop.
Find out more information on the websites.

Downloads

images (.zip)

10.6 Gb @ ~ UHD-1 resolution

META-data + RATINGS (.CSV)

DATASET Paper (.PDF)

Meta-data and ratings (.csv) 

The meta-data file contains only information about the training images. The full details regarding the validation and test sets will be released as the AIM challenge progresses to the next stage. Please find out more information about this on the challenge submission website.

CSV file fields​

• image_name: name of the image, based on a dataset specific ID
• quality_mos: the quality mean opinion score (MOS) of the image
• set: the membership of an image to the training, validation and test sets; only the training set ratings are available publicly before the challenge ends
• orig_width, orig_height: original image dimensions​

• views, likes, favorites, downloads, comments: popularity indicators from the time the image was indexed
• image_bytes: size of the original image in bytes
• user_name: the author account name on Pixabay.com
• page_url: the address of the image post on Pixabay.com

Among the highest quality images

Lowest quality images

Even if the lowest quality images are highly aesthetic, they show degradation that is technical in nature such as blurs, loss of contrast or under-exposure. Removing the confound of aesthetics is a good reason why the database is an interesting challenge for NR-IQA.