KonIQ++: Boosting No-Reference Image Quality Assessment in the Wild by Jointly Predicting Image Quality and Defects
Source code for the BMVC'21 oral "KonIQ++: Boosting No-Reference Image Quality Assessment in the Wild by Jointly Predicting Image Quality and Defects".
The KonIQ++ database can be found here.
Our pretrained model can be found here.
Install dependencies
pip install -r requirements.txt
The code also requires apex, please refer to this repository for instructions.
To reproduce reported results in the paper, first download the koniq++database.csv file from KonIQ++ Webpage and put it in your own path for KonIQ-10k dataset, then run:
python main.py --dataset KonIQ-10k --resize --lr 1e-4 -bs 8 -e 25 --ft_lr_ratio 0.1 --loss_type norm-in-norm --p 1 --q 2 --koniq_root 'YOUR OWN PATH'
Model will be trained and tested on the KonIQ++ dataset.
If you want to cross test on either CLIVE or SPAQ dataset, add following options:
--test_on_clive: Add option to cross test on CLIVE dataset after training on KonIQ++.--clive_root 'YOUR OWN PATH': Root path for CLIVE dataset.--test_on_spaq: Add option to cross test on SPAQ dataset after training on KonIQ++.--spaq_root 'YOUR OWN PATH': Root path for SPAQ dataset.
Note: We have conducted user screenings to the released version KonIQ++ labels, so the results might have small fluctuations regarding to the reported performances. To perfectly reproduce performances in the paper, you can use the Koniq++_old.csv label in the old_label folder as substitution.
To predict single image quality, run:
python test_image.py --root_path 'IMAGE PATH' --img_name 'TEST IMAGE NAME' --resize --p 1 --q 2
To visualize feature heatmaps from two side networks, add option --save_heatmap. Heatmaps will be saved under the image root path.
If you find this work useful for your research, please cite our paper.
This code is largely implemented based upon norm-in-norm IQA, we thank them for their sharing.