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Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
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An experimental PyTorch implementation of DB-CNN is released at! Only support experiment on LIVE IQA right now, other datasets will be added soon!

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network (Official IEEE preprint version)

Weixia Zhang, Kede Ma, Jia Yan, Dexiang Deng, and Zhou Wang

IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), to appear, 2019.

Deep Bilinear Pooling for Blind Image Quality Assessment (Unofficial free version)

Files under distorion_generator are used for synthesizing distorted images.


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 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:

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