👁️ 🖼️ 🔥PyTorch Toolbox for Image Quality Assessment, including 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'

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

Official implementation for "Image Quality Assessment using Contrastive Learning"

A benchmark implementation of representative deep BIQA models

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

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

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

[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

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

Non-local Modeling for Image Quality Assessment

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