Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning

doi:10.1109/TNNLS.2013.2271356

OPT OpenIR > 光谱成像技术研究室

	Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning
	Gao, Xinbo 1; Gao, Fei1 ; Tao, Dacheng2,3 ; Li, Xuelong4
	2013-12-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号	24 期号:12 页码:2013-2026
摘要	Universal blind image quality assessment (IQA) metrics that can work for various distortions are of great importance for image processing systems, because neither ground truths are available nor the distortion types are aware all the time in practice. Existing state-of-the-art universal blind IQA algorithms are developed based on natural scene statistics (NSS). Although NSS-based metrics obtained promising performance, they have some limitations: 1) they use either the Gaussian scale mixture model or generalized Gaussian density to predict the nonGaussian marginal distribution of wavelet, Gabor, or discrete cosine transform coefficients. The prediction error makes the extracted features unable to reflect the change in nonGaussianity (NG) accurately. The existing algorithms use the joint statistical model and structural similarity to model the local dependency (LD). Although this LD essentially encodes the information redundancy in natural images, these models do not use information divergence to measure the LD. Although the exponential decay characteristic (EDC) represents the property of natural images that large/small wavelet coefficient magnitudes tend to be persistent across scales, which is highly correlated with image degradations, it has not been applied to the universal blind IQA metrics; and 2) all the universal blind IQA metrics use the same similarity measure for different features for learning the universal blind IQA metrics, though these features have different properties. To address the aforementioned problems, we propose to construct new universal blind quality indicators using all the three types of NSS, i.e., the NG, LD, and EDC, and incorporating the heterogeneous property of multiple kernel learning (MKL). By analyzing how different distortions affect these statistical properties, we present two universal blind quality assessment models, NSS global scheme and NSS two-step scheme. In the proposed metrics: 1) we exploit the NG of natural images using the original marginal distribution of wavelet coefficients; 2) we measure correlations between wavelet coefficients using mutual information defined in information theory; 3) we use features of EDC in universal blind image quality prediction directly; and 4) we introduce MKL to measure the similarity of different features using different kernels. Thorough experimental results on the Laboratory for Image and Video Engineering database II and the Tampere Image Database2008 demonstrate that both metrics are in remarkably high consistency with the human perception, and overwhelm representative universal blind algorithms as well as some standard full reference quality indexes for various types of distortions.
文章类型	Article
关键词	Exponential Decay Characteristic (Edc) Image Quality Assessment (Iqa) Multiple Kernel Learning (Mkl) Natural Scene Statistics (Nss)
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TNNLS.2013.2271356
收录类别	SCI ; EI
关键词[WOS]	STRUCTURAL SIMILARITY ; CONTOURLET DOMAIN ; INFORMATION ; BLUR ; ALGORITHMS ; JPEG2000 ; FUSION ; INDEX
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000326940600009
引用统计	被引频次：99[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/23472
专题	光谱成像技术研究室
作者单位	1.Xidian Univ, VIPS Lab, Sch Elect Engn, Xian 710071, Peoples R China 2.Univ Technol, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 3.Univ Technol, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Gao, Xinbo,Gao, Fei,Tao, Dacheng,et al. Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2013,24(12):2013-2026.
APA	Gao, Xinbo,Gao, Fei,Tao, Dacheng,&Li, Xuelong.(2013).Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,24(12),2013-2026.
MLA	Gao, Xinbo,et al."Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 24.12(2013):2013-2026.

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