When spatial distribution unites with spatial contrast: an effective blind image quality assessment model. Issue 12 (1st December 2016)

• Record Type: Journal Article
• Authors: Yan, Yaping; Du, Songlin; Zhang, Hongjuan; Ma, Yide
• Is Part Of: IET image processing. Volume 10:Issue 12(2016)
• Page Start: 1017
• Page End: 1028
• Publication Date: 2016-12-01
• Subjects: feature extraction -- Gaussian processes -- statistics -- learning (artificial intelligence); spatial distribution -- spatial contrast -- blind image quality assessment model -- perceptual quality -- human visual system -- structural degradation -- multithreshold local tetra pattern -- MTLTrP -- Weber–Laplacian of Gaussian operator -- WLOG operator -- intensity contrast -- local contrast feature extraction -- joint statistics -- BIQA model learning; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0177
• Languages: English
• ISSNs: 1751-9659

Abstract

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.