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Pytorch Image Quality: Metrics for Image Quality Assessment

2022; RELX Group (Netherlands); Linguagem: Inglês

10.2139/ssrn.4206741

1556-5068

Sergey Kastryulin, Jamil Zakirov, Denis Prokopenko, Dmitry V. Dylov,

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 pop- ular 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 founda- tion of the library, describe the evaluation strategy that makes it reliable, provide the benchmarks that showcase the performance–time trade-o ff s, 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/.