No-Reference Blur Assessment of Digital Pictures Based on Multifeature Classifiers

In this paper, we address the problem of no-reference quality assessment for digital pictures corrupted with blur. We start with the generation of a large real image database containing pictures taken by human users in a variety of situations, and the conduction of subjective tests to generate the ground truth associated to those images. Based upon this ground truth, we select a number of high quality pictures and artificially degrade them with different intensities of simulated blur (gaussian and linear motion), totalling 6000 simulated blur images. We extensively evaluate the performance of state-of-the-art strategies for no-reference blur quantification in different blurring scenarios, and propose a paradigm for blur evaluation in which an effective method is pursued by combining several metrics and low-level image features. We test this paradigm by designing a no-reference quality assessment algorithm for blurred images which combines different metrics in a classifier based upon a neural network structure. Experimental results show that this leads to an improved performance that better reflects the images' ground truth. Finally, based upon the real image database, we show that the proposed method also outperforms other algorithms and metrics in realistic blur scenarios.

[1]  Joonki Paik,et al.  Simultaneous out-of-focus blur estimation and restoration for digital auto-focusing system , 1998 .

[2]  Alan C. Bovik,et al.  41 OBJECTIVE VIDEO QUALITY ASSESSMENT , 2003 .

[3]  Hanghang Tong,et al.  Blur detection for digital images using wavelet transform , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[4]  A. Bovik,et al.  OBJECTIVE VIDEO QUALITY ASSESSMENT , 2003 .

[5]  Bülent Sankur,et al.  Statistical analysis of image quality measures , 2000, 2000 10th European Signal Processing Conference.

[6]  Tony F. Chan,et al.  Total variation blind deconvolution , 1998, IEEE Trans. Image Process..

[7]  J. Paik,et al.  Out-of-focus blur estimation and restoration for digital auto-focusing system , 1998 .

[8]  A. Gruber,et al.  Performance of backpropagation networks in the second-level trigger of the H1-experiment , 1993 .

[9]  Zhou Wang,et al.  Quality-aware images , 2006, IEEE Transactions on Image Processing.

[10]  Xuezheng Liu,et al.  Bayesian motion blur identification using blur priori , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[11]  Zhou Wang,et al.  Local Phase Coherence and the Perception of Blur , 2003, NIPS.

[12]  Bülent Sankur,et al.  Statistical evaluation of image quality measures , 2002, J. Electronic Imaging.

[13]  Jerome L. Myers,et al.  Research Design and Statistical Analysis , 1991 .

[14]  Aggelos K. Katsaggelos,et al.  A VQ-based blind image restoration algorithm , 2003, IEEE Trans. Image Process..

[15]  Jiming Zhou,et al.  Embedded Passive Technology Application: Design and Fabrication of an Automotive Engine Controller , 2005, 2005 Conference on High Density Microsystem Design and Packaging and Component Failure Analysis.

[16]  Sheila S. Hemami,et al.  A metric for continuous quality evaluation of compressed video with severe distortions , 2004, Signal Process. Image Commun..

[17]  Anil K. Jain Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[18]  Mostafa Kaveh,et al.  Blind image restoration by anisotropic regularization , 1999, IEEE Trans. Image Process..

[19]  Constantine Butakoff,et al.  Type of Blur and Blur Parameters Identification Using Neural Network and Its Application to Image Restoration , 2002, ICANN.

[20]  A. Murat Tekalp,et al.  Maximum likelihood parametric blur identification based on a continuous spatial domain model , 1992, IEEE Trans. Image Process..

[21]  Jean-Bernard Martens,et al.  Multidimensional modeling of image quality , 2002, Proc. IEEE.

[22]  Stefan Winkler,et al.  Perceptual blur and ringing metrics: application to JPEG2000 , 2004, Signal Process. Image Commun..

[23]  Sheila S. Hemami,et al.  A scalable wavelet-based video distortion metric and applications , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

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

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

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

[27]  David J. Sakrison,et al.  The effects of a visual fidelity criterion of the encoding of images , 1974, IEEE Trans. Inf. Theory.

[28]  Constantine Butakoff,et al.  Blurred image restoration using the type of blur and blur parameter identification on the neural network , 2002, IS&T/SPIE Electronic Imaging.

[29]  Wei-Ying Ma,et al.  Blur determination in the compressed domain using DCT information , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[30]  Giorgio Bonmassar,et al.  Real-time restoration of images degraded by uniform motion blur in foveal active vision systems , 1999, IEEE Trans. Image Process..

[31]  Boualem Boashash,et al.  The bootstrap and its application in signal processing , 1998, IEEE Signal Process. Mag..

[32]  Lina J. Karam,et al.  No-reference objective wavelet based noise immune image sharpness metric , 2005, IEEE International Conference on Image Processing 2005.

[33]  Lei Zhang,et al.  Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding , 2003, IEEE Trans. Medical Imaging.

[34]  Lina J. Karam,et al.  Human Visual System Based No-Reference Objective Image Sharpness Metric , 2006, 2006 International Conference on Image Processing.

[35]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[36]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[37]  Stéphane Mallat,et al.  Characterization of Signals from Multiscale Edges , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  A. Said,et al.  Objective no-reference image blur metric based on local phase coherence , 2009 .

[39]  M. Cannon Blind deconvolution of spatially invariant image blurs with phase , 1976 .

[40]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[41]  Ben Liang,et al.  Blind image deconvolution using a robust GCD approach , 1999, IEEE Trans. Image Process..

[42]  Michael W. Marcellin,et al.  Blur identification from vector quantizer encoder distortion , 1998, IEEE Trans. Image Process..

[43]  A. Murat Tekalp,et al.  Identification of image and blur parameters for the restoration of noncausal blurs , 1986, IEEE Trans. Acoust. Speech Signal Process..

[44]  Moon Gi Kang,et al.  An Algorithm To Extract Camera-shaking Degree And Noise Variance In The Peak-trace Domain. , 1998, International 1998 Conference on Consumer Electronics.

[45]  Lina J. Karam,et al.  A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) , 2009, IEEE Transactions on Image Processing.