# PyTorch Image Quality: Metrics for Image Quality Assessment

arXiv: https://arxiv.org/abs/2208.14818 · HTML: https://mlsanity.com/paper/2208.14818/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 90 references that are in the map.
- First posted on arXiv 31 Aug 2022.

## Builds on (most cited first, 28 of 28)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Kornia: an Open Source Differentiable Computer Vision Library for PyTorch](https://mlsanity.com/paper/1910.02190/) (2019)
- [A Note on the Inception Score](https://mlsanity.com/paper/1801.01973/) (2018)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration](https://mlsanity.com/paper/2007.12142/) (2020)
- [PieAPP: Perceptual Image-Error Assessment through Pairwise Preference](https://mlsanity.com/paper/1806.02067/) (2018)
- [A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment](https://mlsanity.com/paper/1607.06140/) (2016)
- [Geometry Score: A Method For Comparing Generative Adversarial Networks](https://mlsanity.com/paper/1802.02664/) (2018)
- [The Shape of Data: Intrinsic Distance for Data Distributions](https://mlsanity.com/paper/1905.11141/) (2019)
- [Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator](https://mlsanity.com/paper/1608.07433/) (2016)
- [DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning](https://mlsanity.com/paper/2001.08113/) (2020)
- [Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric](https://mlsanity.com/paper/2011.15002/) (2020)
- [NTIRE 2021 Challenge on Perceptual Image Quality Assessment](https://mlsanity.com/paper/2105.03072/) (2021)
- [KonIQ-10k: Towards an ecologically valid and large-scale IQA database](https://mlsanity.com/paper/1803.08489/) (2018)
- [ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection](https://mlsanity.com/paper/2205.01432/) (2022)

## Built on by (most cited first, 30 of 34)

- [Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing](https://mlsanity.com/paper/2407.01521/) (2024)
- [Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity](https://mlsanity.com/paper/2305.08252/) (2023)
- [DiffGuard: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models](https://mlsanity.com/paper/2308.07687/) (2023)
- [A Control-Centric Benchmark for Video Prediction](https://mlsanity.com/paper/2304.13723/) (2023)
- [Ambient Diffusion Omni: Training Good Models with Bad Data](https://mlsanity.com/paper/2506.10038/) (2025)
- [RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs](https://mlsanity.com/paper/2505.16770/) (2025)
- [UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality Assessment](https://mlsanity.com/paper/2406.17472/) (2024)
- [DriftRec: Adapting diffusion models to blind JPEG restoration](https://mlsanity.com/paper/2211.06757/) (2022)
- [Color Conditional Generation with Sliced Wasserstein Guidance](https://mlsanity.com/paper/2503.19034/) (2025)
- [Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstruction](https://mlsanity.com/paper/2406.15656/) (2024)
- [Gemino: Practical and Robust Neural Compression for Video Conferencing](https://mlsanity.com/paper/2209.10507/) (2022)
- [Reparo: Loss-Resilient Generative Codec for Video Conferencing](https://mlsanity.com/paper/2305.14135/) (2023)
- [AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results](https://mlsanity.com/paper/2408.11982/) (2024)
- [TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models](https://mlsanity.com/paper/2309.11258/) (2023)
- [A Physics-Informed Deep Learning Model for MRI Brain Motion Correction](https://mlsanity.com/paper/2502.09296/) (2025)
- [Straighter Flow Matching via a Diffusion-Based Coupling Prior](https://mlsanity.com/paper/2311.16507/) (2023)
- [Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology](https://mlsanity.com/paper/2312.09792/) (2023)
- [Perceptual Crack Detection for Rendered 3D Textured Meshes](https://mlsanity.com/paper/2405.06143/) (2024)
- [Perceptual Fairness in Image Restoration](https://mlsanity.com/paper/2405.13805/) (2024)
- [Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality](https://mlsanity.com/paper/2505.19696/) (2025)
- [Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models](https://mlsanity.com/paper/2506.13614/) (2025)
- [Many-objective Optimization via Voting for Elites](https://mlsanity.com/paper/2307.02661/) (2023)
- [CGVQM+D: Computer Graphics Video Quality Metric and Dataset](https://mlsanity.com/paper/2506.11546/) (2025)
- [Biology-driven assessment of deep learning super-resolution imaging of the porosity network in dentin](https://mlsanity.com/paper/2510.08407/) (2025)
- [Analyzing and Guiding Zero-Shot Posterior Sampling in Diffusion Models](https://mlsanity.com/paper/2602.07715/) (2026)
- [Distribution-aware Fairness Test Generation](https://mlsanity.com/paper/2305.13935/) (2023)
- [The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning](https://mlsanity.com/paper/2308.07885/) (2023)
- [Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation](https://mlsanity.com/paper/2412.06003/) (2024)
- [DCRA-Net: Attention-Enabled Reconstruction Model for Dynamic Fetal Cardiac MRI](https://mlsanity.com/paper/2412.15342/) (2024)
- [Appeal prediction for AI up-scaled Images](https://mlsanity.com/paper/2502.14013/) (2025)

## Abstract (arXiv)

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/.