NDLI: When spatial distribution unites with spatial contrast: an effective blind image quality assessment model

Content Provider	IET Digital Library
Author	Yan, Yaping Du, Songlin Zhang, Hongjuan Ma, Yide
Abstract	Blind image quality assessment (BIQA), which aims to estimate the perceptual quality of images without any reference information, is a very important yet challenging task. Although human visual system is sensitive to degradations on both spatial contrast and spatial distribution, most of the existing structural degradation based BIQA models consider only one of them. This study introduces a novel BIQA model by taking into account degradations on both contrast and spatial distribution. First, the authors construct a multi-threshold local tetra pattern (MTLTrP) instead of local binary pattern to measure the changes on spatial distribution. Second, Weber–Laplacian of Gaussian (WLOG) operator, which responds to intensity contrast in a small spatial neighbourhood, is proposed to extract local contrast features. Finally, the joint statistics of MTLTrP and WLOG are utilised for BIQA model learning. Experimental results on three large benchmark databases demonstrate that the proposed model outperforms state-of-the-art BIQA models, as well as with several well-known full reference quality assessment methods.
Starting Page	1017
Ending Page	1028
Page Count	12
ISSN	17519659
Volume Number	10
e-ISSN	17519667
Issue Number	Issue 12, Dec (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/10/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0177
Journal	IET Image Processing
Publisher Date	2016-08-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	BIQA Model Learning Blind Image Quality Assessment Model Computer Vision And Image Processing Technique Feature Extraction Gaussian Processes Human Visual System Image Recognition Intensity Contrast Joint Statistics Knowledge Engineering Technique Learning in AI Local Contrast Feature Extraction MTLTrP Multithreshold Local Tetra Pattern Perceptual Quality Spatial Contrast Spatial Distribution Statistics Structural Degradation Weber–Laplacian of Gaussian Operator WLOG Operator
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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