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The PyTorch implementation of DB-CNN is released at https://github.com/zwx8981/DBCNN-PyTorch!

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network

Weixia Zhang, Kede Ma, Jia Yan, Dexiang Deng, and Zhou Wang https://ieeexplore.ieee.org/document/8576582

IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Volume: 30 , Issue: 1 , Jan. 2020.

Files under distorion_generator are used for synthesizing distorted images.

Usuage:

distorted_img = distortion_generator( img, dist_type, level, seed )

Where img is the original pristine image, dist_type refers to a specified distortion type ranging in 1~9.

1, Gaussian Blur
2, White Noise
3, JPEG Compression
4, JPEG2000 Compression
5, Contrast Change
6, Pink Noise
7, Image Color Quantization with Dither
8, Over-Exposure
9, Under-Exposure

level is a specified degradation level range in 1~5.

seed should be fixed to be 1.

Training codes live in dbcnn folder.

Running the run_exp.m script to train and test on a specifid dataset across 10 random splits.

Prerequisite: Matlab(We use 2017a), MatConvNet (We use 1.0-beta25), vlfeat(We use 0.9.2)

Pretrained s-cnn model is included in dbcnn\data\models, you should download vgg-16 model from http://www.vlfeat.org/matconvnet/pretrained/ and put it in dbcnn\data\models.

You need to copy the matconvet/matlab folder to that of your matconvnet to modify the vl_simplenn.m and PDist.m files.

Relevant links:
Waterloo Exploration Database: https://ece.uwaterloo.ca/~k29ma/exploration/
PASCAL VOC 2012: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/

Citation

@article{zhang2020blind,
title={Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network},
author={Zhang, Weixia and Ma, Kede and Yan, Jia and Deng, Dexiang and Wang, Zhou},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
volume={30},
number={1},
pages={36--47},
year={2020}
}

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