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}
}