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

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

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

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"

[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"

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"

Non-local Modeling for Image Quality Assessment

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

Official implementation of our IEEE Access paper (2024), ZEN-IQA: Zero-Shot Explainable and No-Reference Image Quality Assessment with Vision Language Model

Cause the original CEIQ code is written in MATLAB, it is difficult to integrate the model into python codes. This CEIQ model is trained on kadid10k dataset, which contains only 220 images vs 1500+ used in the original model. Therefore, the results may different and not so accurately compared to the original model.

Official repository for MaPLe-IQA