No-Reference Image Quality Assessment Algorithms

No-reference Image Quality Assessment(NIQA) is a task of evaluating an image without a reference image. Since the evaluation algorithm learns the features of good quality images and scores input images, a training process is required.

1. Target Research Papers

1. BRISQUE: Mittal, Anish, Anush Krishna Moorthy, and Alan Conrad Bovik. "No-reference image quality assessment in the spatial domain." IEEE Transactions on Image Processing (TIP) 21.12 (2012): 4695-4708.

2. NIQE: Mittal, Anish, Rajiv Soundararajan, and Alan C. Bovik. "Making a “completely blind” image quality analyzer." IEEE Signal Processing Letters (SPL) 20.3 (2012): 209-212.

3. PIQE: Venkatanath, N., et al. "Blind image quality evaluation using perception based features." 2015 Twenty First National Conference on Communications (NCC). IEEE, 2015.

4. RankIQA: Liu, Xialei, Joost Van De Weijer, and Andrew D. Bagdanov. "Rankiqa: Learning from rankings for no-reference image quality assessment." Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2017.

5. MetaIQA: Zhu, Hancheng, et al. "MetaIQA: Deep meta-learning for no-reference image quality assessment." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020.

2. Dependencies

I used the following libraries in Windows 10.

python == 3.9.7

pillow == 8.4.0

tqdm == 4.62.3

pytorch == 1.10.1

torchvision == 0.11.2

opencv-python == 4.5.4.60

scipy == 1.7.1

pandas == 1.3.4

3. Quick Start

Download the pre-trained model checkpoint files.

1. RankIQA: https://drive.google.com/drive/folders/1Y2WgNHL6vowvKA0ISGUefQiggvrCL5rl?usp=sharing

   default directory: ./RankIQA/Rank_live.caffemodel.pt

2. MetaIQA: https://drive.google.com/drive/folders/1SCo56y9s0yB-TPcnVHqoc63TZ2ngSxPG?usp=sharing

   default directory: ./MetaIQA/metaiqa.pth

Windows User

• Run demo1.bat & demo2.bat in the windows terminal.

Linux User

• Run demo1.sh & demo2.sh in the linux terminal.

Check "options.py" as well. The demo files are tutorials.

The demo images are from KADID10K dataset: http://database.mmsp-kn.de/kadid-10k-database.html

4. Acknowledgements

Repositories

1. BRISQUE(↓): https://github.com/spmallick/learnopencv/blob/master/ImageMetrics/Python/brisquequality.py
2. NIQE(↓): https://github.com/guptapraful/niqe
3. NIQE model parameters: https://github.com/csjunxu/Bovik_NIQE_SPL2013
4. PIQE(↓): https://github.com/buyizhiyou/NRVQA
5. RankIQA(↓): https://github.com/YunanZhu/Pytorch-TestRankIQA
6. MetaIQA(↑): https://github.com/zhuhancheng/MetaIQA

Images

1. KADID10K: http://database.mmsp-kn.de/kadid-10k-database.html

5. Author

Dae-Young Song

M.S. Student, Department of Electronics Engineering, Chungnam National University

Github: https://github.com/EadCat