waihoh / nssadnn_iqa

Pytorch version of IEEE Transactions on Multimedia 2019: "Naturalness-Aware Deep No-Reference Image Quality Assessment."

NSSADNN_IQA

Pytorch version of IEEE Transactions on Multimedia 2019 : B. Yan, B. Bare and W. Tan, "Naturalness-Aware Deep No-Reference Image Quality Assessment," in IEEE Transactions on Multimedia, vol. 21, no. 10, pp. 2603-2615, Oct. 2019, doi: 10.1109/TMM.2019.2904879.

Note

*I did not use the learning rate that used in the paper: 0.01, because the ideal result cannot be obtained when the initial learning rate is 0.01, so the initial learning rate set here is 0.001 instead of 0.01. *This training progress only support on LIVE II database now, the training progress on TID2013, CSIQ, LIVEMD, CLIVE will be released soon.

Train

python train.py

TODO

• Cross dataset test code will be published
• Train on different distortion types on LIVE, TID2013, CSIQ will be published
• Code of evaluations on Waterloo Exploration Database (D-test, L-test, P-test and gMAd competition) will be published