No-reference quality assessment for contrast-distorted images based on multifaceted statistical representation of structure

In many real-world applications, images are prone to be degraded by contrast distortions during image acquisition. Quality assessment for contrast-distorted images is vital for benchmarking and optimizing the contrast-enhancement algorithms. To this end, this paper proposes a no-reference quality metric for contrast-distorted images based on Multifaceted Statistical representation of Structure (MSS). The “Multifaceted” has two meanings, namely (1) not only the luminance information, but also the chromatic information is used for structure representation. This is inspired by the fact that the chromatic information on the one hand affects the perception of image quality as well, and on the other hand it changes along with the contrast distortions. Therefore, the chromatic information should be integrated with the luminance information for quality assessment of contrast-distorted images, a fact most existing quality metrics overlook; (2) regarding structure representation, three aspects, i.e. spatial intensity, spatial distribution, and orientation of structures are calculated, which is enlightened by the fact that the human visual system (HVS) is sensitive to the three aspects of structures. Specifically, the image is first transformed from RGB to the S-CIELAB color space to obtain a representation that is more consistent with the characteristics of the HVS, as well as to separate the chromatic information from the luminance. Then the statistical structural features are extracted from both luminance and chromatic channels. Finally, the back propagation (BP) neural network is adopted to train a quality prediction model. Experimental results conducted on four public contrast-distorted image databases demonstrate the superiority of the proposed method to the relevant state-of-the-arts.

[1]  Jeng-Shyang Pan,et al.  No-Reference Quality Metric of Blocking Artifacts Based on Color Discontinuity Analysis , 2014, IEICE Trans. Inf. Syst..

[2]  Weisi Lin,et al.  No-Reference and Robust Image Sharpness Evaluation Based on Multiscale Spatial and Spectral Features , 2017, IEEE Transactions on Multimedia.

[3]  Fei Zhou,et al.  Full-Reference Quality Assessment of Contrast Changed Images Based on Local Linear Model , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[4]  Ke Gu,et al.  An efficient and effective blind camera image quality metric via modeling quaternion wavelet coefficients , 2017, J. Vis. Commun. Image Represent..

[5]  Aníbal R. Figueiras-Vidal,et al.  A Dynamically Adjusted Mixed Emphasis Method for Building Boosting Ensembles , 2008, IEEE Transactions on Neural Networks.

[6]  Wenjun Zhang,et al.  No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization , 2017, IEEE Transactions on Cybernetics.

[7]  Weisi Lin,et al.  Image Sharpness Assessment by Sparse Representation , 2016, IEEE Transactions on Multimedia.

[8]  Xingming Sun,et al.  Detecting seam carving based image resizing using local binary patterns , 2015, Comput. Secur..

[9]  Frederic Dufaux,et al.  A subjective study of the influence of color information on visual quality assessment of high resolution pictures , 2009 .

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

[11]  Yi Wan,et al.  Joint image dehazing and contrast enhancement using the HSV color space , 2015, 2015 Visual Communications and Image Processing (VCIP).

[12]  Zhou Wang,et al.  Unified Blind Quality Assessment of Compressed Natural, Graphic, and Screen Content Images , 2017, IEEE Transactions on Image Processing.

[13]  Weisi Lin,et al.  Learning Structural Regularity for Evaluating Blocking Artifacts in JPEG Images , 2014, IEEE Signal Processing Letters.

[14]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Weisi Lin,et al.  GridSAR: Grid strength and regularity for robust evaluation of blocking artifacts in JPEG images , 2015, J. Vis. Commun. Image Represent..

[16]  M.A. Joshi,et al.  Quality Analysis of Color Images Compressed with Enhanced Vector Quantizer Designed Using HSI Color Space , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

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

[18]  Chul Lee,et al.  Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition , 2017, J. Vis. Commun. Image Represent..

[19]  Md. Arifur Rahman,et al.  Image contrast enhancement based on intensity expansion-compression , 2017, J. Vis. Commun. Image Represent..

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

[21]  Wen Gao,et al.  Image quality assessment based on local orientation distributions , 2010, 28th Picture Coding Symposium.

[22]  Mislav Grgic,et al.  Blind image sharpness assessment based on local contrast map statistics , 2018, J. Vis. Commun. Image Represent..

[23]  Guangming Shi,et al.  Orientation selectivity based visual pattern for reduced-reference image quality assessment , 2016, Inf. Sci..

[24]  Ingrid Heynderickx,et al.  The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images , 2016, IEEE Transactions on Image Processing.

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

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

[27]  Xuelong Li,et al.  Image quality assessment based on S-CIELAB model , 2011, Signal Image Video Process..

[28]  Songlin Du,et al.  When spatial distribution unites with spatial contrast: an effective blind image quality assessment model , 2016, IET Image Process..

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

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

[31]  B. Roe,et al.  Boosted decision trees as an alternative to artificial neural networks for particle identification , 2004, physics/0408124.

[32]  Wenjun Zhang,et al.  Hybrid No-Reference Quality Metric for Singly and Multiply Distorted Images , 2014, IEEE Transactions on Broadcasting.

[33]  Wenjun Zhang,et al.  Automatic Contrast Enhancement Technology With Saliency Preservation , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[34]  P. Lambert,et al.  PDE-Based Enhancement of Color Images in RGB Space , 2012, IEEE Transactions on Image Processing.

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

[36]  Hua Huang,et al.  Image Quality Assessment Using Directional Anisotropy Structure Measurement , 2017, IEEE Transactions on Image Processing.

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

[38]  Qingjie Zhao,et al.  Blind image quality assessment by relative gradient statistics and adaboosting neural network , 2016, Signal Process. Image Commun..

[39]  Rabab Kreidieh Ward,et al.  Fast Image/Video Contrast Enhancement Based on Weighted Thresholded Histogram Equalization , 2007, IEEE Transactions on Consumer Electronics.

[40]  Deshi Li,et al.  No-Reference Quality Assessment of Noise-Distorted Images Based on Frequency Mapping , 2017, IEEE Access.

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

[42]  Alex ChiChung Kot,et al.  A Fast Approach for No-Reference Image Sharpness Assessment Based on Maximum Local Variation , 2014, IEEE Signal Processing Letters.

[43]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[44]  Damon M. Chandler,et al.  No-reference image quality assessment based on log-derivative statistics of natural scenes , 2013, J. Electronic Imaging.

[45]  Guangming Shi,et al.  Blind Quality Index for Multiply Distorted Images Using Biorder Structure Degradation and Nonlocal Statistics , 2018, IEEE Transactions on Multimedia.

[46]  M. Landy,et al.  Orientation-selective adaptation to first- and second-order patterns in human visual cortex. , 2006, Journal of neurophysiology.

[47]  Boting Rex Lin,et al.  Fuzzy automatic contrast enhancement based on fuzzy C-means clustering in CIELAB color space , 2016, 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA).

[48]  Zhou Wang,et al.  No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics , 2015, IEEE Signal Processing Letters.

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

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

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

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