👁️ 🖼️ 🔥PyTorch Toolbox for Image Quality Assessment, including PSNR, SSIM, LPIPS, FID, NIQE, NRQM(Ma), MUSIQ, TOPIQ, NIMA, DBCNN, BRISQUE, PI and more...

A comprehensive collection of IQA papers

[CVPR2023] Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective

[unofficial] CVPR2014-Convolutional neural networks for no-reference image quality assessment

Collection of Blind Image Quality Metrics in Matlab

The repository for 'Uncertainty-aware blind image quality assessment in the laboratory and wild' and 'Learning to blindly assess image quality in the laboratory and wild'

Official implementation for "Image Quality Assessment using Contrastive Learning"

[unofficial] Pytorch implementation of WaDIQaM in TIP2018, Bosse S. et al. (Deep neural networks for no-reference and full-reference image quality assessment)

[WACV 2024 Oral] - ARNIQA: Learning Distortion Manifold for Image Quality Assessment

A benchmark implementation of representative deep BIQA models

Official implementation for CVPR2023 Paper "Re-IQA : Unsupervised Learning for Image Quality Assessment in the Wild"

[unofficial] PyTorch Implementation of image quality assessment methods: IQA-CNN++ in ICIP2015 and IQA-CNN in CVPR2014

[official] No reference image quality assessment based Semantic Feature Aggregation, published in ACM MM 2017, TMM 2019

ACM MM 2019 SGDNet: An End-to-End Saliency-Guided Deep Neural Network for No-Reference Image Quality Assessment

Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment

[ICME2024, Official Code] for paper "Bringing Textual Prompt to AI-Generated Image Quality Assessment"

Universal Perturbation Attack on differentiable no-reference image- and video-quality metrics

Non-local Modeling for Image Quality Assessment

Fast Adversarial CNN-based Perturbation Attack on no-reference image- and video-quality metrics

An implementation of the NIMA paper on the TID2013 dataset, using PyTorch.