Official PyTorch implementation of Re-IQA, a No-Reference Image Quality Assessment algorithm proposed in IEEE/CVF CVPR 2023. Re-IQA achieves SoTA performance across popular NR-IQA databases : KonIQ, FLIVE, SPAQ, CLIVE, LIVE-IQA, CSIQ-IQA, TID-2013 and KADID.

Useful Links : Preprint OpenAccess Supplementary YouTube Video Slides Poster

The code has been tested on Linux systems with python 3.9, pytorch 1.13.1, torchvision 0.14.1 and CUDA 11.7. Other dependencies can be installed via requirements.txt.

We follow the data training pipeline provided in CONTRIQUE. Create a directory mkdir training_data to store images used for training RE-IQA-Quality-Aware Module.

1. KADIS-700k : Download KADIS-700k dataset. Store this data in the training_data/kadis700k directory. Note we do not use the synthetically distorted images as in CONTRIQUE.
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. We only use the images labelled as "naturally-blurred" from the training (220) and evaluation (411) sets. We have renumbered the images in the provided csv file.
5. VOC : VOC images are stored in the training_data/UGC_images/VOC2012 directory.

We use DDP Training across 6 nodes.

python main_contrast.py --method MoCov2 --cosine --head mlp --multiprocessing-distributed --csv_path ./csv_files/moco_train.csv --model_path ./expt0  --optimizer LARS --tb_path ./expt0 -j 28 --batch_size 630 --learning_rate 12 --n_aug 11  --epochs 40 --n_scale 2 --n_distortions 1 --patch_size 160 --world-size 6 --warm --swap_crops 1 --dist-url tcp://[your first node address]:[specified port] --rank 0

On the 5 other nodes, run the same command with --rank 1-5

Clarifications : In the paper submitted to CVPR, we had reported training for 25 epochs. However, we observed more stabilized IQA results when trained for 40 epochs. Thus, we recommend training for 40 epochs. Also, the earlier implementation of the paper, a learning rate of 0.6 was used with batch size scaling. The learning rate scaling formula based on batch size used was ((batch_size * 2 scales) / 64 * 0.6 ≈ 12). However, in the current code, we have simplified it to directly take the scaled learning rate as an input rather than calculating it using the scaling formula.

Download the quality-aware trained model from Google Drive and store in a folder named re-iqa_ckpts. Finally, to obtain Re-IQA Quality-Aware features, run

python demo_quality_aware_feats.py --head mlp

We utilized the vanilla MoCo-v2 training code using ResNet-50 architecture and ImageNet database provided in the PyContrast repository to train our Content Aware Module using the default settings.

Download the content-aware trained model from Google Drive and store in a folder named re-iqa_ckpts. Finally, to obtain Re-IQA Content-Aware features, run

python demo_content_aware_feats.py --head mlp

We used Sklearn's Linear Regression Models with Regularization for training the final IQA model. We recommend using either one of Ridge or Elastic Net models and performing extensive hyper-parameter search for each database to extract maximum performance.

If you use this code for your research, please cite the following paper:

A. Saha, S. Mishra, and A. C. Bovik, “Re-IQA : Unsupervised Learning for Image Quality Assessment in the Wild,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, https://doi.org/10.48550/arXiv.2304.00451.

@InProceedings{Saha_2023_CVPR,
    author    = {Saha, Avinab and Mishra, Sandeep and Bovik, Alan C.},
    title     = {Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {5846-5855}
}

Our Code is based on PyContrast. We thank the Yonglong Tian for making the code available.

• Avinab Saha ( [email protected] ) -- Graduate student, LIVE, Dept. of ECE, UT Austin.
• Sandeep Mishra ( [email protected] ) -- Graduate student, LIVE, Dept. of ECE, UT Austin.