An effective general-purpose NR-IQA model using natural scene statistics (NSS) of the luminance relative order

Abstract Blind/no-reference image quality assessment (NR-IQA) aims to assess the quality of an image without any reference image. In this paper, we propose an effective and efficient general-purpose NR-IQA model using natural scene statistics (NSS) of the luminance relative order, based on the observation that the variation of the marginal distribution of the relative order coefficients effectively reflect the degree of warping caused by different types of image distortions. In the literature, gradient-relevant methods have had a big success in full-reference (FR) IQA and reduced-reference (RR) IQA. Inspired by these, we extend it to NR-IQA in this paper. Notice that the NSS-based models usually extract their features derived from the spatial, wavelet, DCT and spectral domain etc. Unlike these metrics, the proposed method firstly extracts 32 natural scene statistics features of the luminance relative order, obtained from the log histograms of log horizontal, vertical, main-diagonal and secondary-diagonal derivatives, along with kurtosis, variance, differential entropy and entropy at two scales. Then a mapping is learned to predict the quality score using a support vector regression. The experimental results on several benchmark databases showed that the proposed method is comparable with the state-of-the-art methods and has a relatively low complexity.

[1]  Shan Suthaharan.  No-reference visually significant blocking artifact metric for natural scene images , 2009, Signal Process..

[2]  Alan C. Bovik,et al.  A Two-Step Framework for Constructing Blind Image Quality Indices , 2010, IEEE Signal Processing Letters.

[3]  Weisi Lin,et al.  Blind Image Quality Assessment Using Statistical Structural and Luminance Features , 2016, IEEE Transactions on Multimedia.

[4]  Christophe Charrier,et al.  A DCT Statistics-Based Blind Image Quality Index , 2010, IEEE Signal Processing Letters.

[5]  Christophe Charrier,et al.  Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain , 2012, IEEE Transactions on Image Processing.

[6]  David J. Sheskin,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 1997 .

[7]  Dirk Tomandl,et al.  A Modified General Regression Neural Network (MGRNN) with new, efficient training algorithms as a robust 'black box'-tool for data analysis , 2001, Neural Networks.

[8]  Mahmood Amiri,et al.  BAM Learning of Nonlinearly Separable Tasks by Using an Asymmetrical Output Function and Reinforcement Learning , 2009, IEEE Transactions on Neural Networks.

[9]  Weisi Lin,et al.  No-Reference Image Blur Assessment Based on Discrete Orthogonal Moments , 2016, IEEE Transactions on Cybernetics.

[10]  Lei Zhang,et al.  Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features , 2014, IEEE Transactions on Image Processing.

[11]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[12]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[13]  Xiaojun Wu,et al.  Blind Image Quality Assessment Using a General Regression Neural Network , 2011, IEEE Transactions on Neural Networks.

[14]  Eero P. Simoncelli,et al.  On Advances in Statistical Modeling of Natural Images , 2004, Journal of Mathematical Imaging and Vision.

[15]  Jiejin Cai,et al.  Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks , 2009 .

[16]  Alan C. Bovik,et al.  A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.

[17]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[18]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[19]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[20]  Nikolay N. Ponomarenko,et al.  TID2008 – A database for evaluation of full-reference visual quality assessment metrics , 2004 .

[21]  Ivo F. Sbalzarini,et al.  A Natural-Scene Gradient Distribution Prior and its Application in Light-Microscopy Image Processing , 2016, IEEE Journal of Selected Topics in Signal Processing.

[22]  Yuukou Horita,et al.  No reference image quality assessment for JPEG2000 based on spatial features , 2008, Signal Process. Image Commun..

[23]  Xiangchu Feng,et al.  Edge Strength Similarity for Image Quality Assessment , 2013, IEEE Signal Processing Letters.

[24]  Hua Huang,et al.  No-reference image quality assessment based on spatial and spectral entropies , 2014, Signal Process. Image Commun..

[25]  Eric C. Larson,et al.  Most apparent distortion: full-reference image quality assessment and the role of strategy , 2010, J. Electronic Imaging.

[26]  Guangquan Cheng,et al.  Image quality assessment using natural image statistics in gradient domain , 2011 .

[27]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[28]  David S. Doermann,et al.  No-Reference Image Quality Assessment Using Visual Codebooks , 2012, IEEE Transactions on Image Processing.

[29]  Ivo F. Sbalzarini,et al.  Image Enhancement by Gradient Distribution Specification , 2014, ACCV Workshops.

[30]  Weisi Lin,et al.  No-Reference Image Sharpness Assessment in Autoregressive Parameter Space , 2015, IEEE Transactions on Image Processing.

[31]  Alan C. Bovik,et al.  Massive Online Crowdsourced Study of Subjective and Objective Picture Quality , 2015, IEEE Transactions on Image Processing.

[32]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[33]  Alan C. Bovik,et al.  Perceptual quality prediction on authentically distorted images using a bag of features approach , 2016, Journal of vision.

[34]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[35]  Weisi Lin,et al.  Image Quality Assessment Based on Gradient Similarity , 2012, IEEE Transactions on Image Processing.

[36]  H. Shu,et al.  Blind Image Quality Assessment Using Natural Scene Statistics in the Gradient Domain , 2014, 2014 8th Asia Modelling Symposium.

[37]  Lei Zhang,et al.  Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index , 2013, IEEE Transactions on Image Processing.

[38]  Alan C. Bovik,et al.  Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality , 2011, IEEE Transactions on Image Processing.