  Image Quality Assessment (IQA) metrics are widely used to quantitatively
estimate the extent of image degradation following some forming, restoring,
transforming, or enhancing algorithms. We present PyTorch Image Quality (PIQ),
a usability-centric library that contains the most popular modern IQA
algorithms, guaranteed to be correctly implemented according to their original
propositions and thoroughly verified. In this paper, we detail the principles
behind the foundation of the library, describe the evaluation strategy that
makes it reliable, provide the benchmarks that showcase the performance-time
trade-offs, and underline the benefits of GPU acceleration given the library is
used within the PyTorch backend. PyTorch Image Quality is an open source
software: https://github.com/photosynthesis-team/piq/.
