Automatic no-reference image quality assessment

No-reference image quality assessment aims to predict the visual quality of distorted images without examining the original image as a reference. Most no-reference image quality metrics which have been already proposed are designed for one or a set of predefined specific distortion types and are unlikely to generalize for evaluating images degraded with other types of distortion. There is a strong need of no-reference image quality assessment methods which are applicable to various distortions. In this paper, the authors proposed a no-reference image quality assessment method based on a natural image statistic model in the wavelet transform domain. A generalized Gaussian density model is employed to summarize the marginal distribution of wavelet coefficients of the test images, so that correlative parameters are needed for the evaluation of image quality. The proposed algorithm is tested on three large-scale benchmark databases. Experimental results demonstrate that the proposed algorithm is easy to implement and computational efficient. Furthermore, our method can be applied to many well-known types of image distortions, and achieves a good quality of prediction performance.

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

[2]  Ling Shao,et al.  Non-distortion-specific no-reference image quality assessment: A survey , 2015, Inf. Sci..

[3]  Xinbo Gao,et al.  No-reference image quality assessment in contourlet domain , 2010, Neurocomputing.

[4]  Zhou Wang,et al.  Reduced-reference image quality assessment using a wavelet-domain natural image statistic model , 2005, IS&T/SPIE Electronic Imaging.

[5]  Alan C. Bovik,et al.  No-reference quality assessment using natural scene statistics: JPEG2000 , 2005, IEEE Transactions on Image Processing.

[6]  R. SheikhH.,et al.  No-reference quality assessment using natural scene statistics , 2005 .

[7]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

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

[9]  Zheng-guang Xie,et al.  An image compression method using sparse representation and grey relation , 2013, Proceedings of 2013 IEEE International Conference on Grey systems and Intelligent Services (GSIS).

[10]  B. Potocnik,et al.  Image enhancement by using directional wavelet transform , 2006, 28th International Conference on Information Technology Interfaces, 2006..

[11]  Minh N. Do,et al.  Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance , 2002, IEEE Trans. Image Process..

[12]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[13]  Chen Yao,et al.  No-reference quality assessment for DCT-based compressed image , 2015, J. Vis. Commun. Image Represent..

[14]  Minh N. Do,et al.  Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models , 2002, IEEE Trans. Multim..

[15]  Stefan Winkler,et al.  A no-reference perceptual blur metric , 2002, Proceedings. International Conference on Image Processing.

[16]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

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

[18]  Ching Y. Suen,et al.  A novel Non-local means image denoising method based on grey theory , 2016, Pattern Recognit..

[19]  Jong Bae Kim,et al.  Multiresolution-based watersheds for efficient image segmentation , 2003, Pattern Recognit. Lett..

[20]  Ching Y. Suen,et al.  Robust face recognition based on dynamic rank representation , 2016, Pattern Recognit..

[21]  Xuelong Li,et al.  Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Zhimin Zhao,et al.  A novel image denoising algorithm in wavelet domain using total variation and grey theory , 2010 .

[23]  Martin J. Wainwright,et al.  Image denoising using scale mixtures of Gaussians in the wavelet domain , 2003, IEEE Trans. Image Process..

[24]  Lionel Moisan,et al.  No-Reference Image Quality Assessment and Blind Deblurring with Sharpness Metrics Exploiting Fourier Phase Information , 2015, Journal of Mathematical Imaging and Vision.

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

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

[27]  Eero P. Simoncelli,et al.  Image compression via joint statistical characterization in the wavelet domain , 1999, IEEE Trans. Image Process..

[28]  Weisi Lin,et al.  Image Quality Assessment Using Multi-Method Fusion , 2013, IEEE Transactions on Image Processing.

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

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

[31]  David Smith,et al.  A continuous wavelet transform algorithm for peak detection , 2008, Electrophoresis.