This repository provides the reference implementation for the paper
Curiously Effective Features for Image Quality Prediction
which has been accepted for publication at ICIP 2021 (Link to preprint).

@misc{becker2021curiously,
      title={Curiously Effective Features for Image Quality Prediction}, 
      author={S\"oren Becker and Thomas Wiegand and Sebastian Bosse},
      year={2021},
      eprint={2106.05946},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

You can reproduce our results with the following steps:

1. Set up a python environment using the provided CuriousFeatures.yml file.
2. Download the datasets and use format_datasets.py to bring the data into the expected format.
3. Run bash feature_extraction.sh in your terminal. This will extract features according to the paper from all images in all databases. We provide two scripts here, feature_extraction.sh and feature_extraction_reproduce.sh. feature_extraction.sh uses PyTorch and runs considerably faster than feature_extraction_reproduce.sh, however, results will likely deviate slightly from the results reported in the paper. If you want to reproduce our results and do not care about computational speed, you can use feature_extraction_reproduce.sh.
4. Open the jupyter notebook Experiments.ipynb and follow the instructions therein to reproduce reported correlations.

License

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