Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35899
Title: Blind image quality assessment using joint statistics of gradient magnitude and laplacian features
Authors: Xue, WF
Mou, XQ
Zhang, L 
Bovik, AC
Feng, XC
Keywords: Blind image quality assessment
Gradient magnitude
LOG
Jointly adaptive normalization
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2014, v. 23, no. 11, p. 4850-4862 How to cite?
Journal: IEEE transactions on image processing 
Abstract: Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of a distorted image without information regarding its reference image. Existing BIQA models usually predict the image quality by analyzing the image statistics in some transformed domain, e. g., in the discrete cosine transform domain or wavelet domain. Though great progress has been made in recent years, BIQA is still a very challenging task due to the lack of a reference image. Considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we propose a novel BIQA model that utilizes the joint statistics of two types of commonly used local contrast features: 1) the gradient magnitude (GM) map and 2) the Laplacian of Gaussian (LOG) response. We employ an adaptive procedure to jointly normalize the GM and LOG features, and show that the joint statistics of normalized GM and LOG features have desirable properties for the BIQA task. The proposed model is extensively evaluated on three large-scale benchmark databases, and shown to deliver highly competitive performance with state-of-the-art BIQA models, as well as with some well-known full reference image quality assessment models.
URI: http://hdl.handle.net/10397/35899
ISSN: 1057-7149 (print)
1941-0042 (online)
DOI: 10.1109/TIP.2014.2355716
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

49
Last Week
2
Last month
Citations as of Jan 21, 2017

WEB OF SCIENCETM
Citations

37
Last Week
0
Last month
Citations as of Jan 23, 2017

Page view(s)

12
Last Week
0
Last month
Checked on Jan 22, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.