SFA-PLSR (Test code) for the following paper: Li, Dingquan, Tingting Jiang, and Ming Jiang. "Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images." Proceedings of the 2017 ACM on Multimedia Conference. ACM, 2017.
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!
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.
Please cite our paper if it helps your research:
@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}
}
Dingquan Li, [email protected].