Paper Info
Title
Blind Image Quality Assessment Using Semi-supervised Rectifier Networks
Abstract
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 addresses these difficulties by learning a robust, nonlinear kernel regression function using a rectifier neural network. The method is pre-trained with unlabeled data and fine-tuned with labeled data. It generalizes across a large set of images and distortion types without the need for a large amount of labeled data. We evaluate our approach on two benchmark datasets and show that it not only outperforms the current state of the art in blind image quality estimation, but also outperforms the state of the art in non-blind measures. Furthermore, we show that our semi-supervised approach is robust to using varying amounts of labeled data.
Year
DOI
Venue
2014
10.1109/CVPR.2014.368
CVPR
Keywords
Field
DocType
nonlinear kernel regression function,learning (artificial intelligence),regression analysis,distortion,nonblind measure,quality score,image distortion,blind image quality assessment,nonlinear functions,labeled data,computer vision,machine learning,semi-supervised rectifier neural network
Semi-supervised learning,Computer science,Image quality,Artificial intelligence,Labeled data,Artificial neural network,Distortion,Kernel regression,Computer vision,Rectifier,Pattern recognition,Generalization,Machine learning
Conference
ISSN
Citations 
PageRank 
1063-6919
27
0.83
References 
Authors
10
3
Name
Order
Citations
PageRank
Huixuan Tang11134.81
Neel Joshi2115563.95
Ashish Kapoor31833119.72