Blind Image Quality Assessment Using Semi-supervised Rectifier Networks

@article{Tang2014BlindIQ,
  title={Blind Image Quality Assessment Using Semi-supervised Rectifier Networks},
  author={Huixuan Tang and Neel Joshi and Ashish Kapoor},
  journal={2014 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2014},
  pages={2877-2884}
}
It is often desirable to evaluate images quality with a perceptually relevant measure that does not require a reference image. Recent approaches to this problem use human provided quality scores with machine learning to learn a measure. The biggest hurdles to these efforts are: 1) the difficulty of generalizing across diverse types of distortions and 2) collecting the enormity of human scored training data that is needed to learn the measure. We present a new blind image quality measure that… CONTINUE READING
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