Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli and Alan C. Bovik

This is the official repository of the paper Image Quality Assessment using Contrastive Learning

The code has been tested on Linux systems with python 3.6. Please refer to requirements.txt for installing dependent packages.

In order to obtain quality score using CONTRIQUE model, checkpoint needs to be downloaded. The following command can be used to download the checkpoint.

wget -L https://utexas.box.com/shared/static/rhpa8nkcfzpvdguo97n2d5dbn4qb03z8.tar -O models/CONTRIQUE_checkpoint25.tar -q --show-progress

Alternatively, the checkpoint can also be downloaded using this link.

Google drive link for the checkpoint link.

We provide trained regressor models in models directory which can be used for predicting image quality using features obtained from CONTRIQUE model. For demonstration purposes, some sample images provided in the sample_images folder.

For blind quality prediction, the following commands can be used.

python3 demo_score.py --im_path sample_images/60.bmp --model_path models/CONTRIQUE_checkpoint25.tar --linear_regressor_path models/CLIVE.save
python3 demo_score.py --im_path sample_images/img66.bmp --model_path models/CONTRIQUE_checkpoint25.tar --linear_regressor_path models/LIVE.save

For Full-reference quality assessment, the folllowing command can be employed.

python3 demos_score_FR.py --ref_path sample_images/churchandcapitol.bmp --dist_path sample_images/img66.bmp --model_path models/CONTRIQUE_checkpoint25.tar --linear_regressor_path models/CSIQ_FR.save

For calculating CONTRIQUE features, the following commands can be used. The features are saved in '.npy' format.

python3 demo_feat.py --im_path sample_images/60.bmp --model_path models/CONTRIQUE_checkpoint25.tar --feature_save_path features.npy
python3 demo_feat.py --im_path sample_images/img66.bmp --model_path models/CONTRIQUE_checkpoint25.tar --feature_save_path features.npy

Create a directory mkdir training_data to store images used for training CONTRIQUE.

1. KADIS-700k : Download KADIS-700k dataset and execute the supllied codes to generate synthetically distorted images. Store this data in the training_data/kadis700k directory.
2. AVA : Download AVA dataset and store in the training_data/UGC_images/AVA_Dataset directory.
3. COCO : COCO dataset contains 330k images spread across multiple competitions. We used 4 folders training_data/UGC_images/test2015, training_data/UGC_images/train2017, training_data/UGC_images/val2017, training_data/UGC_images/unlabeled2017 for training.
4. CERTH-Blur : Blur dataset images are stored in the training_data/UGC_images/blur_image directory.
5. VOC : VOC images are stored in the training_data/UGC_images/VOC2012 directory.

Download csv files containing path to images and corresponding distortion classes.

wget -L https://utexas.box.com/shared/static/124n9sfb27chgt59o8mpxl7tomgvn2lo.csv -O csv_files/file_names_ugc.csv -q --show-progress
wget -L https://utexas.box.com/shared/static/jh5cmu63347auyza37773as5o9zxctby.csv -O csv_files/file_names_syn.csv -q --show-progress

The above files can also be downloaded manually using these links link1, link2 Google drive links link1, link2

For training with a single GPU the following command can be used

python3 train.py --batch_size 256 --lr 0.6 --epochs 25

Training with multiple GPUs using Distributed training (Recommended)

Run the following commands on different terminals concurrently

CUDA_VISIBLE_DEVICES=0 python3 train.py --nodes 4 --nr 0 --batch_size 64 --lr 0.6 --epochs 25
CUDA_VISIBLE_DEVICES=1 python3 train.py --nodes 4 --nr 1 --batch_size 64 --lr 0.6 --epochs 25
CUDA_VISIBLE_DEVICES=2 python3 train.py --nodes 4 --nr 2 --batch_size 64 --lr 0.6 --epochs 25
CUDA_VISIBLE_DEVICES=3 python3 train.py --nodes 4 --nr 3 --batch_size 64 --lr 0.6 --epochs 25

Note that in distributed training, batch_size value will be the number of images to be loaded on each GPU. During CONTRIQUE training equal number of images will be loaded from both synthetic and authentic distortions. Thus in the above example code, 128 images will be loaded on each GPU.

After CONTRIQUE model training is complete, a linear regressor is trained using CONTRIQUE features and corresponding ground truth quality scores using the following command.

python3 train_regressor.py --feat_path feat.npy --ground_truth_path scores.npy --alpha 0.1

Please contact Pavan (pavan.madhusudana@gmail.com) if you have any questions, suggestions or corrections to the above implementation.

@article{madhusudana2021st,
  title={Image Quality Assessment using Contrastive Learning},
  author={Madhusudana, Pavan C and Birkbeck, Neil and Wang, Yilin and Adsumilli, Balu and Bovik, Alan C},
  journal={arXiv:2110.13266},
  year={2021}
}