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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 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MRD: Using Physically Based Differentiable Rendering to Probe Vision Models for 3D Scene Understanding

    cs.CV 2025-12 conditional novelty 6.0 of 10

    MRD finds physically different 3D scenes that reproduce a target model activation, revealing which shape and material properties vision models are sensitive to.

  2. RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs

    cs.CV 2025-05 reject novelty 6.0 of 10

    The RBench-V benchmark finds that the best current AI models score 25.8%, far below 82.3% for humans, on visual reasoning problems claimed to require multi-modal outputs.

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

    cs.CV 2025-05 conditional novelty 4.0 of 10

    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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