(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.