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PyTorch Image Quality: Metrics for Image Quality Assessment

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arxiv 2208.14818 v1 pith:ROCRS6W4 submitted 2022-08-31 eess.IV cs.CV

Sergey Kastryulin , Jamil Zakirov , Denis Prokopenko , Dmitry V. Dylov This is my paper · ORCID
classification eess.IVcs.CV
keywords imagequalitypytorchlibraryalgorithmsassessmentmetricsused
verification ladder T0 review T1 audit T2 compute T3 formal
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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/.

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Cited by 5 Pith papers

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    TransformAR-KD+ uses knowledge distillation with category-aware teachers and cross-attention decoders to improve AR image quality prediction, achieving SRCC 0.8411 on ARIQA.

  4. Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality

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    A position paper contending that multimedia quality assessment should move beyond scalar Mean Opinion Score toward context-aware, explainable, and multimodal modeling.

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