No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics

Contrast distortion is often a determining factor in human perception of image quality, but little investigation has been dedicated to quality assessment of contrast-distorted images without assuming the availability of a perfect-quality reference image. In this letter, we propose a simple but effective method for no-reference quality assessment of contrast distorted images based on the principle of natural scene statistics (NSS). A large scale image database is employed to build NSS models based on moment and entropy features. The quality of a contrast-distorted image is then evaluated based on its unnaturalness characterized by the degree of deviation from the NSS models. Support vector regression (SVR) is employed to predict human mean opinion score (MOS) from multiple NSS features as the input. Experiments based on three publicly available databases demonstrate the promising performance of the proposed method.

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

[2]  Yair Weiss,et al.  Scale invariance and noise in natural images , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[3]  E. Adelson,et al.  Image statistics and the perception of surface qualities , 2007, Nature.

[4]  Dapeng Wu,et al.  BNB Method for No-Reference Image Quality Assessment , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Xuelong Li,et al.  An image quality assessment metric based contourlet , 2008, 2008 15th IEEE International Conference on Image Processing.

[6]  W. Geisler.  Visual perception and the statistical properties of natural scenes. , 2008, Annual review of psychology.

[7]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[8]  Apurba Das,et al.  Image Enhancement in Spatial Domain , 2018, Understanding Digital Image Processing.

[9]  Guangming Shi,et al.  Reduced-Reference Image Quality Assessment With Visual Information Fidelity , 2013, IEEE Transactions on Multimedia.

[10]  Sophie Triantaphillidou,et al.  Spatial contrast sensitivity and discrimination in pictorial images , 2014, Electronic Imaging.

[11]  Alan C. Bovik,et al.  Image information and visual quality , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[12]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[13]  Min Liu,et al.  Exploiting global and local information for image quality assessment with contrast change , 2015, 2015 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting.

[14]  Weisi Lin,et al.  No-reference noticeable blockiness estimation in images , 2008, Signal Process. Image Commun..

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

[16]  Xuelong Li,et al.  Image Quality Assessment Based on Multiscale Geometric Analysis , 2009, IEEE Transactions on Image Processing.

[17]  Zhou Wang,et al.  A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images , 2015, IEEE Signal Processing Letters.

[18]  Wenjun Zhang,et al.  Subjective and objective quality assessment for images with contrast change , 2013, 2013 IEEE International Conference on Image Processing.

[19]  Zhou Wang,et al.  Modern Image Quality Assessment , 2006, Modern Image Quality Assessment.

[20]  Mohammed Ghanbari,et al.  Scope of validity of PSNR in image/video quality assessment , 2008 .

[21]  Sos S. Agaian,et al.  Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy , 2007, IEEE Transactions on Image Processing.

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

[23]  Abdul Rehman,et al.  Reduced-Reference Image Quality Assessment by Structural Similarity Estimation , 2012, IEEE Transactions on Image Processing.

[24]  Lei Zhang,et al.  A Feature-Enriched Completely Blind Image Quality Evaluator , 2015, IEEE Transactions on Image Processing.

[25]  Sophie Triantaphillidou,et al.  Contrast sensitivity and discrimination in pictorial images , 2014 .

[26]  Zhou Wang,et al.  No-reference image sharpness assessment based on local phase coherence measurement , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[27]  Ashis Kumar Dhara,et al.  Performance metrics for image contrast , 2011, 2011 International Conference on Image Information Processing.

[28]  Krista A. Ehinger,et al.  SUN database: Large-scale scene recognition from abbey to zoo , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[29]  Chun-Ling Yang,et al.  Gradient-Based Structural Similarity for Image Quality Assessment , 2006, 2006 International Conference on Image Processing.

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

[31]  Weisi Lin,et al.  Perceptual visual quality metrics: A survey , 2011, J. Vis. Commun. Image Represent..

[32]  Weisi Lin,et al.  The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement , 2016, IEEE Transactions on Cybernetics.

[33]  Wenjun Zhang,et al.  Using Free Energy Principle For Blind Image Quality Assessment , 2015, IEEE Transactions on Multimedia.

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

[35]  Alan C. Bovik,et al.  RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment , 2012, IEEE Transactions on Image Processing.

[36]  Weisi Lin,et al.  A Psychovisual Quality Metric in Free-Energy Principle , 2012, IEEE Transactions on Image Processing.

[37]  Yücel Altunbasak,et al.  A Histogram Modification Framework and Its Application for Image Contrast Enhancement , 2009, IEEE Transactions on Image Processing.

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

[39]  Xiaolin Wu,et al.  A Linear Programming Approach for Optimal Contrast-Tone Mapping , 2011, IEEE Transactions on Image Processing.

[40]  Nikolay N. Ponomarenko,et al.  Color image database TID2013: Peculiarities and preliminary results , 2013, European Workshop on Visual Information Processing (EUVIP).

[41]  John C. Russ,et al.  Image Enhancement in the Spatial Domain , 2015 .

[42]  Weisi Lin,et al.  Fourier Transform-Based Scalable Image Quality Measure , 2012, IEEE Transactions on Image Processing.

[43]  Nikolay N. Ponomarenko,et al.  Image database TID2013: Peculiarities, results and perspectives , 2015, Signal Process. Image Commun..