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No reference image quality assessment based Semantic Feature Aggregation, published in ACM MM 2017, TMM 2019
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README.md

Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?

License

Description

SFA code for the following papers:

Requirement

Framework: Caffe 1.0 + MATLAB 2016b Interface

The PLSR model uesd in the test code is trained on LIVE gblur images.

Download the ResNet-50-model.caffemodel from https://github.com/KaimingHe/deep-residual-networks and paste it into the directory "models/" before using the code!

Note for training

All we need to train is a PLSR model, where the training function is plsregress.m in MATLAB. The features are extracted from the DCNN models pre-trained on the image classification task.

Update: remember to change the value of "im_dir" and "im_lists" in data info.

Citation

Please cite our papers if it helps your research:

@arcticle{li2018which,
  title={Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?},
  author={Li, Dingquan and Jiang, Tingting and Lin, Weisi and Jiang, Ming},
  booktitle={IEEE Transactions on Multimedia},
  volume={21}, 
  number={5}, 
  pages={1221-1234},  
  month={May},
  year={2019}, 
  doi={10.1109/TMM.2018.2875354}
}

[Paper]

@inproceedings{li2017exploiting,
  title={Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images},
  author={Li, Dingquan and Jiang, Tingting and Jiang, Ming},
  booktitle={Proceedings of the 2017 ACM on Multimedia Conference},
  pages={378--386},
  year={2017},
  organization={ACM}
}

[Paper] [Poster]

Contact

Dingquan Li, dingquanli AT pku DOT edu DOT cn.

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