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Pytorch version of the CVPR2014 paper: L. Kang, P. Ye, Y. Li and D. Doermann, "Convolutional Neural Networks for No-Reference Image Quality Assessment," 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 1733-1740, doi: 10.1109/CVPR.2014.224.

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CNNIQA_LeKang

Pytorch version of the CVPR2014 paper: L. Kang, P. Ye, Y. Li and D. Doermann, "Convolutional Neural Networks for No-Reference Image Quality Assessment," 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 1733-1740, doi: 10.1109/CVPR.2014.224.

Note

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

Thanks

This code is based on the changes of LiDingquan

About

Pytorch version of the CVPR2014 paper: L. Kang, P. Ye, Y. Li and D. Doermann, "Convolutional Neural Networks for No-Reference Image Quality Assessment," 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 1733-1740, doi: 10.1109/CVPR.2014.224.

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