NDLI: Image quality assessment based on complementary local feature extraction and quantification

Content Provider	IET Digital Library
Author	Zhao, Yang Ding, Yong Zhao, Xin Yu
Abstract	A novel full reference image quality assessment method based on statistical local representation from two complementary sources: log-Gabor wavelet representation and local derivative pattern is presented. The dissimilarity of these extracted features between distorted and reference images is quantified and mapped into an objective quality score. Experimental results on large-scale database show that the proposed method not only outperforms the state-of-the-art methods in terms of high accuracy of image quality prediction, but also is robust across different distortion types with high computation efficient.
Starting Page	1849
Ending Page	1851
Page Count	3
ISSN	00135194
Volume Number	52
e-ISSN	1350911X
Issue Number	Issue 22, Oct (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/52/22
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2016.1328
Journal	Electronics Letters
Publisher Date	2016-10-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Complementary Local Feature Extraction Complementary Local Feature Quantification Computer Vision And Image Processing Technique Feature Extraction Full Reference Image Quality Assessment Method Image Distortion Image Recognition Image Representation Large-scale Database Local Derivative Pattern Log-Gabor Wavelet Representation Statistical Analysis Statistical Local Representation Statistics
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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