Welcome to PIQ’s documentation!

_images/piq_logo_main.png

Note

PyTorch Image Quality (PIQ) is not endorsed by Facebook, Inc.

PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

PyTorch Image Quality (PIQ) is a collection of measures and metrics for image quality assessment. PIQ helps you to concentrate on your experiments without the boilerplate code. The library contains a set of measures and metrics that is continually getting extended. For measures/metrics that can be used as loss functions, corresponding PyTorch modules are implemented.

We provide:

  • Unified interface, which is easy to use and extend.

  • Written on pure PyTorch with bare minima of additional dependencies.

  • Extensive user input validation. Your code will not crash in the middle of the training.

  • Fast (GPU computations available) and reliable.

  • Most metrics can be backpropagated for model optimization.

  • Supports python 3.7-3.10.

PIQ was initially named PhotoSynthesis.Metrics.

Citation

If you use PIQ in your project, please, cite it as follows.

@misc{kastryulin2022piq,
  title = {PyTorch Image Quality: Metrics for Image Quality Assessment},
  url = {https://arxiv.org/abs/2208.14818},
  author = {Kastryulin, Sergey and Zakirov, Jamil and Prokopenko, Denis and Dylov, Dmitry V.},
  doi = {10.48550/ARXIV.2208.14818},
  publisher = {arXiv},
  year = {2022}
}
@misc{piq,
  title={{PyTorch Image Quality}: Metrics and Measure for Image Quality Assessment},
  url={https://github.com/photosynthesis-team/piq},
  note={Open-source software available at https://github.com/photosynthesis-team/piq},
  author={Sergey Kastryulin and Dzhamil Zakirov and Denis Prokopenko},
  year={2019}
}

Indices and tables