NDLI: No-reference image quality assessment in complex-shearlet domain

Content Provider	Springer Nature Link
Author	Mahmoudpour, Saeed Kim, Manbae
Copyright Year	2016
Abstract	The field of image quality measure (IQM) is growing rapidly in recent years. In particular, there was a significant progress in no-reference (NR) IQM methods. Natural scenes have certain statistical properties which vary in the presence of distortion. The statistical changes represent the loss of naturalness and can be efficiently quantified using shearlet transformation of images. In this paper, a general-purpose NR IQM approach is proposed based on the statistical characteristics of natural images in shearlet domain. The method utilizes a set of distortion-sensitive features extracted from statistical properties of shearlet coefficients. Phase and amplitude of an image contain important perceptual information; therefore, a complex version of the shearlet transform is employed to take advantage of phase and amplitude features in quality estimation. In quality prediction step, the features are used to train image classification and quality prediction models using a support vector machine. The experimental results show that the proposed NR IQM is highly correlated with subjective assessment and outperforms several full-reference and state-of-art NR IQMs.
Starting Page	1465
Ending Page	1472
Page Count	8
File Format	PDF
ISSN	18631703
Journal	Signal, Image and Video Processing
Volume Number	10
Issue Number	8
e-ISSN	18631711
Language	English
Publisher	Springer London
Publisher Date	2016-08-19
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image quality Shearlet transform Natural scene statistics Support vector machine Signal,Image and Speech Processing Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Multimedia Information Systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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