On the Use of Bi-RNN for Image Quality Assessment

Contents

1. Abstract

2. Performance Benchmark

   2.1. Scores comparison on authentic distortions' databases

   2.2. Scores comparison on synthethic datasets

3. Model Zoo

4. Usage

Abstract

The deployment of Deep Neural Networks (DNNs) based on Convolutional Neural Networks (CNNs) pipelines as feature extractors has led to an impressive rise of performance on different computer vision tasks. However many challenges are encountered while dealing with DNNs in the No Reference Image Quality Assessment (NR-IQA) context of which in particular the non uniform distribution of a global quality across the different areas of the the assessed images. We propose a Bi-directional Recurrent Neural Network (RNN) approach that aims to overcome this issue.

Performance Benchmark

Scores comparison on authentic distortions' datasets

Live in the wild dataset

Metric	SROCC ↑	PLCC ↑	KRCC ↑
BRISQUE	0.5710	0.5954	0.4034
NIQE	0.3879	0.4274	0.2637
VGG16	0.8058	0.7996	0.6189
Bi-RCNN(VGG16)	0.8223	0.8568	0.6368
Resnet50	0.8186	0.8361	0.6341
Bi-RCNN(Resnet)	0.8229	0.8514	0.6357

Koniq dataset

Metric	SROCC ↑	PLCC ↑	KRCC ↑
BRISQUE	0.5710	0.5954	0.4743
NIQE	0.3879	0.4274	0.0536
VGG16	0.8058	0.7996	0.5906
Bi-RCNN(VGG16)	0.8223	0.8568	0.6160
Resnet50	0.8186	0.8361	0.6468
Bi-RCNN(Resnet)	0.8229	0.8514	0.6669

Scores comparison on synthethic datasets

Live dataset

Metric	SROCC ↑	PLCC ↑	KRCC ↑
PSNR	0.8907	0.9221	0.7236
SSIM	0.8895	0.9130	0.7239
GMSD	0.9447	0.9439	0.8086
FSIM	0.9517	0.9444	0.8260
VIF	0.9017	0.9235	0.7326
BRISQUE	0.9382	0.9475	0.7878
NIQE	0.6668	0.6440	0.4743
VGG16	0.9605	0.9646	0.8305
Bi-RCNN(VGG16)	0.9826	0.9783	0.8945
Resnet50	0.9652	0.9706	0.8418
Bi-RCNN(Resnet)	0.984	0.9861	0.9036

Kadid10k dataset

Metric	SROCC ↑	PLCC ↑	KRCC ↑
PSNR	0.6910	0.6969	0.5016
SSIM	0.6796	0.6698	0.4969
GMSD	0.8467	0.8461	0.6619
FSIM	0.8353	0.8317	0.6453
VIF	0.6345	0.6360	0.4681
BRISQUE	0.6378	0.6585	0.4695
NIQE	0.3380	0.3994	0.2279
VGG16	0.9412	0.9482	0.7884
Bi-RCNN(VGG16)	0.9581	0.9595	0.8262
Resnet50	0.9385	0.9394	0.7852
Bi-RCNN(Resnet)	0.9638	0.9657	0.8369

CSIQ dataset

Metric	SROCC ↑	PLCC ↑	KRCC ↑
PSNR	0.7776	0.8054	0.5736
SSIM	0.7647	0.7536	0.5680
GMSD	0.9580	0.9526	0.8164
FSIM	0.9226	0.9086	0.7612
VIF	0.6901	0.7609	0.5073
BRISQUE	0.8219	0.8588	0.6457
NIQE	0.6459	0.6600	0.4605
VGG16	0.9753	0.9818	0.8684
Bi-RCNN(VGG16)	0.9781	0.9791	0.8758
Resnet50	0.9809	0.9852	0.8877
Bi-RCNN(Resnet)	0.9829	0.9849	0.8909

TID2013 dataset

Metric	SROCC ↑	PLCC ↑	KRCC ↑
PSNR	0.6912	0.6832	0.4964
SSIM	0.6198	0.6243	0.4393
GMSD	0.8108	0.8535	0.6375
FSIM	0.8043	0.8529	0.6275
VIF	0.5844	0.6959	0.4305
BRISQUE	0.7510	0.7754	0.5684
NIQE	0.2526	0.2842	0.1675
VGG16	0.9421	0.9492	0.7956
Bi-RCNN(VGG16)	0.9526	0.9547	0.815
Resnet50	0.9357	0.9441	0.7843
Bi-RCNN(Resnet)	0.9596	0.9648	0.8307

Live Multi Distortions dataset

Metric	SROCC ↑	PLCC ↑	KRCC ↑
PSNR	0.5300	0.5682	0.3842
SSIM	0.4563	0.4856	0.3072
GMSD	0.8500	0.8666	0.6591
FSIM	0.8658	0.8814	0.6786
VIF	0.6309	0.6479	0.4716
BRISQUE	0.8363	0.8474	0.6586
NIQE	0.4692	0.6170	0.3492
VGG16	0.9712	0.9695	0.8635
Bi-RCNN(VGG16)	0.9704	0.9664	0.8565
Resnet50	0.9788	0.9752	0.8815
Bi-RCNN(Resnet)	0.9734	0.9741	0.865

Model Zoo

weights of the GRU RNN tested on five different folds are also available along with their corresponding IDs splits here.

Usage

The source code is available in the notebook.

• Please either download the datasets or update the DataGenerator's parametters when creating a an instance of it.
• Please note that the Data Generator expects the IDs to be a list stored in a pickle file.

1. Install dependencies, import required libraries and download required datasests.
2. Create an instance of the data generator as follows:

training_generator = DataGenerator(list_IDs_path='./IDs.pickle',overlapping=0,
                    db_path='./Koniq/512x384/',batch_size=1,dim=(224,224), n_channels=3,
                    n_output=1, shuffle=False, part='train',base='resnet')
val_generator = DataGenerator(list_IDs_path='./IDs.pickle',overlapping=0,
                    db_path='./Koniq/512x384/',batch_size=1,dim=(224,224), n_channels=3,
                    n_output=1, shuffle=False, part='test',base='resnet')

3. Create an instance of the CNN network:

 base_model =  Base_Model('resnet',weights='imagenet', include_top=False, input_shape=(224, 224, 3))     

Then build the feature extractor model.

4. Extract the features using the predict function:

 X_train = model_cnn.predict_generator(generator=training_generator)
 X_test = model_cnn.predict_generator(generator=val_generator)

5. Define the RNN model
6. Load y and train the model on the train set.