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All Projects → lidq92 → WaDIQaM

lidq92 / WaDIQaM

Licence: other
[unofficial] Pytorch implementation of WaDIQaM in TIP2018, Bosse S. et al. (Deep neural networks for no-reference and full-reference image quality assessment)

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deep-neural-networkspytorchimage-quality-assessmentblind-image-quality-assessmentfull-reference-iqa

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(Wa)DIQaM-FR/NR

PyTorch 1.1 (with Python 3.6) implementation of the following paper:

Bosse S, Maniry D, Müller K R, et al. Deep neural networks for no-reference and full-reference image quality assessment. IEEE Transactions on Image Processing, 2018, 27(1): 206-219.

You can refer to the chainer codes (only the test part) from the original authors: dmaniry/deepIQA

Note

  • The hyper-parameter or some other experimental settings are not the same as the paper described, e.g., nonoverlapping patches are considered for validation/test images instead of random selection. Readers can refer to the paper for the exact settings of the original paper.
  • Warning!. The performance on each database is not guaranteed using the default settings of the code. Reproduced results are welcomed to reported.
  • If you do not have enough memory, then change slightly the code in IQADataset class. Specifically, read image in __getitem__ instead of __init__. You can choose to use IQADataset_less_memory class instead.

TODO (If I have free time)

  • Reproduce the results on some common databases, especially for the NR model (Currently, NR model is not tuned to reproduce the results.)
  • Simplify the code
  • etc.
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