(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.

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.