This repository provides an implementation for our paper
Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network
(https://www.sciencedirect.com/science/article/pii/S1051200418308868)
article{bosse2019estimation,
title={Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network},
author={Bosse, Sebastian and Becker, S{\"o}ren and M{\"u}ller, Klaus-Robert and Samek, Wojciech and Wiegand, Thomas},
journal={Digital Signal Processing},
volume={91},
pages={54--65},
year={2019},
publisher={Elsevier}
}
The code is written in python and uses pytorch. Required packages and the versions we used are listed in 'requirements.txt'. The implementation consists of 4 files:
iqaDataFrame.py acts like an adapter for the various image quality datasets that brings them into a consistent format. Formating is currently implemented for the liveIQA, TID2013, and CSIQ datasets. Please have a look at the documentation in _format_liveiqa(), _format_tid2013() and _format_csiq() on how to use this class.
iqaDataset.py transforms an iqaDataFrame into a dataset that can be used with a pytorch dataloader.
model.py implements the archticture of the convolutional neural network.
You can use this file to evaluate a pre-trained model. Pre-trained models for various distortion types can be found in the models directory. Please have a look at evaluate.sh to see how to use this code to evaluate a pre-trained model.
If you would like to obtain the code for training a model, please contact Sören Becker (soeren.becker at hhi.fraunhofer.de).
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