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: python demo.py
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}
}
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