UNIQUE
The codebase for
Uncertainty-aware blind image quality assessment in the laboratory and wild (TIP2021)
and
Learning to blindly assess image quality in the laboratory and wild (ICIP2020)
Prequisite:
Python 3+
PyTorch 1.4+
Matlab
Successfully tested on Ubuntu18.04, other OS (i.e., other Linux distributions, Windows)should also be ok.
Usage
Sampling image pairs from multiple databases
data_all.m
Combining the sampled pairs to form the training set
combine_train.m
Training on multiple databases for 10 sessions
python Main.py --train True --network basecnn --representation BCNN --ranking True --fidelity True --std_modeling True --std_loss True --margin 0.025 --batch_size 128 --batch_size2 32 --image_size 384 --max_epochs 3 --lr 1e-4 --decay_interval 3 --decay_ratio 0.1 --max_epochs2 12
(As for ICIP version, set std_loss to False and sample pairs from TID2013 instead of KADID-10K.) (For training with binary labels, set fideliy and std_modeling to False.)
Output predicted quality scores and stds
python Main.py --train False --get_scores True
Result anlysis
Compute SRCC/PLCC after nonlinear mapping: result_analysis.m
Compute fidelity loss: eval_fidelity.m
Pre-trained weights
Google: https://drive.google.com/file/d/18oPH4lALm8mSdZh3fWK97MVq9w3BbEua/view?usp=sharing
Baidu: https://pan.baidu.com/s/1KKncQIoQcbxj7fQlSKUBIQ code：yyev
A basic demo that predict the quality of single images.
python demo.py
(Make sure that the model has been appropriately set to eval mode !)
Link to download the BID dataset
The BID dataset may be difficult to find online, we provide links here:
Google: https://drive.google.com/drive/folders/1Qmtp-Fo1iiQiyf-9uRUpO-YAAM0mcIey?usp=sharing
Baidu: https://pan.baidu.com/s/1TTyb0FJzUdP6muLSbVN3hQ code: ptg0
Training/Testing Data
In addition to the source MATLAB code to generate training/testing data, you may also find the generated files here (If you do not want to generate them yourselve or if you do not have MATLAB):
Google: https://drive.google.com/file/d/1u-6xmedUB0PNA5xM787OY-YfiJg195xA/view
Baidu: https://pan.baidu.com/s/12nb6OTUxnz_rxssg2rthIQ code: 82k3
Citation
@article{zhang2021uncertainty,
title={Uncertainty-aware blind image quality assessment in the laboratory and wild},
author={Zhang, Weixia and Ma, Kede and Zhai, Guangtao and Yang, Xiaokang},
journal={IEEE Transactions on Image Processing},
volume = {30},
pages = {3474--3486},
month = {Mar.},
year={2021}
}
@inproceedings{zhang2020learning,
title={Learning to blindly assess image quality in the laboratory and wild},
author={Zhang, Weixia and Ma, Kede and Zhai, Guangtao and Yang, Xiaokang},
booktitle={IEEE International Conference on Image Processing},
pages={111--115},
year={2020}
}