×

zbMATH — the first resource for mathematics

Blind image quality assessment for Gaussian blur images using exact Zernike moments and gradient magnitude. (English) Zbl 1349.94024
Summary: Features that exhibit human perception on the effect of blurring on digital images are useful in constructing a blur image quality metric. In this paper, we show some of the Exact Zernike Moments (EZMs) that closely model the human quality scores for images of varying degrees of blurriness can be used to measure these distortions. A theoretical framework is developed to identify these EZMs. Together with the selected EZMs, the Gradient Magnitude (GM), which measures the contrast information, is used as a weight in the formulation of the proposed blur metric. The design of the proposed metric consists of two stages. In the first stage, the EZM differences and the GM dissimilarities between the edge points of the test image and the same re-blurred image are extracted. Next, the mean of the weighted EZM features are then pooled to produce a quality score using support vector machine regressor (SVR). We compare the performance of the proposed blur metric with other state-of-the-art full-reference (FR) and no-reference (NR) blur metrics on three benchmark databases. The results using Pearsons Correlation Coefficient (CC) and Spearmans Ranked-Order Correlation Coefficient (SROCC) for the LIVE image database are 0.9659 and 0.9625 respectively. Similarly, high correlations with the subjective scores are achieved for the other two databases as well.
MSC:
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
Software:
LIBSVM; Matlab; TID2013
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] G. Amayeh, G. Bebis, A. Erol, M. Nicolescu, Peg-free hand shape verification using high order Zernike moments, in: 2006 Conference on Computer Vision and Pattern Recognition Workshop, CVPRW׳06, 2006, p. 40.
[2] D.Q. Bao, Gpvm image blur metric matlab code [online], 〈http://www.mathworks.com/matlabcentral/fileexchange/24676-image-blur-metric〉, 2014.
[3] C. Batten, Autofocusing and astigmatism correction in the scanning electron microscope (Ph.D. thesis), Faculty of the Department of Engineering, University of Cambridge, UK, 2000.
[4] Caviedes, J.; Oberti, F., A new sharpness metric based on local kurtosis, edge and energy information, Signal Process.: Image Commun., 19, 147-161, (2004)
[5] C.C. Chang, C.J. Lin, Libsvm: A Library for Support Vector Machines, Software, 2001.
[6] Chen, B.; Shu, H.; Zhang, H.; Coatrieux, G.; Luo, L.; Coatrieux, J. L., Combined invariants to similarity transformation and to blur using orthogonal Zernike moments, IEEE Trans. Image Process., 20, 345-360, (2011) · Zbl 1372.94038
[7] N. Chern, P. Neow, M. Ang Jr., Practical issues in pixel-based autofocusing for machine vision, in: IEEE International Conference on Robotics and Automation, 2001, Proceedings 2001 ICRA, IEEE, vol. 3, 2001, pp. 2791-2796.
[8] C.W. Chong, R. Mukundan, R. Paramesran, An efficient algorithm for fast computation of Zernike moments, in: Proceedings of the 6th Joint Conference on Information Science, JCIS׳02, Research Triangle Park, NC, 2002, pp. 785-788.
[9] Crete, F.; Dolmiere, T.; Ladret, P.; Nicolas, M., The blur effectperception and estimation with a new no-reference perceptual blur metric, Hum. Vis. Electron. Imaging XII, 6492, 11, (2007)
[10] Dudani, S.; Breeding, K.; McGhee, R., Aircraft identification by moment invariants, IEEE Trans. Comput., 100, 39-46, (1977)
[11] Erasmus, S.; Smith, K., An automatic focusing and astigmatism correction system for the sem and ctem, J. Microsc., 127, 185-199, (1982)
[12] R. Ferzli, L. Karam, No-reference objective wavelet based noise immune image sharpness metric, in: IEEE International Conference on Image Processing, vol. 1, 2005, pp. 405-408.
[13] Ferzli, R.; Karam, L., A no-reference objective image sharpness metric based on the notion of just noticeable blur (jnb), IEEE Trans. Image Process., 18, 717-728, (2009) · Zbl 1371.94127
[14] R. Ferzli, L. Karam, Ivu lab [online], 〈http://ivulab.asu.edu/〉, 2014.
[15] Firestone, L.; Cook, K.; Culp, K.; Talsania, N.; Preston, K., Comparison of autofocus methods for automated microscopy, Cytometry, 12, 195-206, (1991)
[16] Ghosal, S.; Mehrotra, R., Orthogonal moment operators for subpixel edge detection, Pattern Recognit., 26, 295-306, (1993)
[17] Hu, M. K., Visual pattern recognition by moment invariants, IRE Trans. Inf. Theory, 8, 179-187, (1962) · Zbl 0102.13304
[18] Iscan, Z.; Dokur, Z.; Ölmez, T., Tumor detection by using Zernike moments on segmented magnetic resonance brain images, Expert Syst. Appl., 37, 2540-2549, (2010)
[19] E. Larson, D.M. Chandler, Categorical image quality (csiq) database 2009 [online], 〈http://vision.okstate.edu/csiq〉, 2009.
[20] Li, C.; Yuan, W.; Bovik, A.; Wu, X., No-reference blur index using blur comparisons, Electron. Lett., 47, 962-963, (2011)
[21] Lim, C. L.; Honarvar, B.; Thung, K. H.; Paramesran, R., Fast computation of exact Zernike moments using cascaded digital filters, Inf. Sci., 181, 3638-3651, (2011)
[22] Liyun, W.; Hefei, L.; Fuhao, Z.; Zhengding, L.; Zhendi, W., Spermatogonium image recognition using Zernike moments, Comput. Methods Progr. Biomed., 95, 10-22, (2009)
[23] Manap, R. A.; Shao, L., Non-distortion-specific no-reference image quality assessmenta survey, Inf. Sci., 301, 141-160, (2015)
[24] X. Marichal, W. Ma, H. Zhang, Blur determination in the compressed domain using dct information, in: Proceedings of 1999 International Conference on Image Processing, 1999, ICIP 99, IEEE, vol. 2, 1999, pp. 386-390.
[25] Marziliano, P.; Dufaux, F.; Winkler, S.; Ebrahimi, T., Perceptual blur and ringing metricsapplication to jpeg2000, Signal Process.: Image Commun., 19, 163-172, (2004)
[26] Mittal, A.; Moorthy, A. K.; Bovik, A. C., No-reference image quality assessment in the spatial domain, IEEE Trans. Image Process., 21, 4695-4708, (2012) · Zbl 1373.94286
[27] Moorthy, A. K.; Bovik, A. C., Blind image quality assessmentfrom natural scene statistics to perceptual quality, IEEE Trans. Image Process., 20, 3350-3364, (2011) · Zbl 1374.94266
[28] Mukundan, R.; Ong, S.; Lee, P. A., Image analysis by tchebichef moments, IEEE Trans. Image Process., 10, 1357-1364, (2001) · Zbl 1037.68782
[29] Narvekar, N.; Karam, L., A no-reference image blur metric based on the cumulative probability of blur detection (cpbd), IEEE Trans. Image Process., 20, 2678-2683, (2011) · Zbl 1372.94189
[30] M. Novotni, R. Klein, 3d Zernike descriptors for content based shape retrieval, in: Proceedings of the Eighth ACM Symposium on Solid Modeling and Applications, ACM, 2003, pp. 216-225.
[31] Paschos, G.; Radev, I.; Prabakar, N., Image content-based retrieval using chromaticity moments, IEEE Trans. Knowl. Data Eng., 15, 1069-1072, (2003)
[32] N. Ponomarenko, K. Egiazarian, Tampere image database 2008 tid2008 [online], 〈http://www.ponomarenko.info/tid2008.htm〉, 2008.
[33] Saad, M. A.; Bovik, A. C.; Charrier, C., Blind image quality assessmenta natural scene statistics approach in the dct domain, IEEE Trans. Image Process., 21, 3339-3352, (2012) · Zbl 1373.94355
[34] D. Shaked, I. Tastl, Sharpness measure: Towards automatic image enhancement, in: IEEE International Conference on Image Processing, ICIP 2005, vol. 1, IEEE, 2005, pp. I-937.
[35] H. Sheikh, Z. Wang, L. Cormack, A. Bovik, Live image quality assessment database release 2 [online], 〈http://live.ece.utexas.edu/research/quality/subjective.htm〉, 2005.
[36] Sheikh, H. R.; Sabir, M. F.; Bovik, A. C., A statistical evaluation of recent full reference image quality assessment algorithms, IEEE Trans. Image Process., 15, 3440-3451, (2006)
[37] Shen, J.; Li, Q.; Erlebacher, G., Hybrid no-reference natural image quality assessment of noisy, blurry, jpeg2000, and jpeg images, IEEE Trans. Image Process., 20, 2089-2098, (2011) · Zbl 1372.94232
[38] Teague, M. R., Image analysis via the general theory of moments, J. Opt. Soc. Am., 70, 920-930, (1980)
[39] Teh, C.; Chin, R., On image analysis by the methods of moments, IEEE Trans. Pattern Anal. Mach. Intell., 10, 496-513, (1988) · Zbl 0709.94543
[40] Thung, K. H.; Paramesran, R.; Lim, C. L., Content-based image quality metric using similarity measure of moment vectors, Pattern Recognit., 45, 2193-2204, (2012) · Zbl 1234.68453
[41] H. Tong, M. Li, H. Zhang, C. Zhang, Blur detection for digital images using wavelet transform, in: 2004 IEEE International Conference on Multimedia and Expo, ICME׳04, vol. 1, IEEE, 2004, pp. 17-20.
[42] Wang, Z.; Bovik, A.; Sheikh, H.; Simoncelli, E., Image quality assessmentfrom error visibility to structural similarity, IEEE Trans. Image Process., 13, 600-612, (2004)
[43] Wang, Z.; Bovik, A. C., Modern image quality assessment, Synth. Lect. Image Video Multimed. Process., 2, 1-156, (2006)
[44] Z. Wang, E.P. Simoncelli, A.C. Bovik, Multiscale structural similarity for image quality assessment, in: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, vol. 2, IEEE, 2004, pp. 1398-1402.
[45] Wee, C.; Paramesran, R., On the computational aspects of Zernike moments, Image Vis. Comput., 25, 967-980, (2007)
[46] Wee, C. Y.; Paramesran, R.; Mukundan, R.; Jiang, X., Image quality assessment by discrete orthogonal moments, Pattern Recognit., 43, 4055-4068, (2010) · Zbl 1211.68489
[47] Xue, W.; Mou, X.; Zhang, L.; Bovik, A. C.; Feng, X., Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features, IEEE Trans. Image Process., 23, 4850-4862, (2014) · Zbl 1374.94417
[48] P. Yap, R. Paramesran, Content-based image retrieval using Legendre chromaticity distribution moments, in: IEE Proceedings - Vision, Image and Signal Processing, vol. 153, IET, 2006, pp. 17-24.
[49] P. Yap, P. Raveendran, Image focus measure based on Chebyshev moments, in: IEE Proceedings - Vision, Image and Signal Processing, vol. 151, IET, 2004, pp. 128-136.
[50] Yap, P. T.; Paramesran, R.; Ong, S. H., Image analysis by krawtchouk moments, IEEE Trans. Image Process., 12, 1367-1377, (2003)
[51] Zhang, L.; Zhang, D.; Mou, X., Fsima feature similarity index for image quality assessment, IEEE Trans. Image Process., 20, 2378-2386, (2011) · Zbl 1373.62333
[52] Zhang, N.; Vladar, A.; Postek, M.; Larrabee, B., A kurtosis-based statistical measure for two-dimensional processes and its application to image sharpness, Proc. Sect. Phys. Eng. Sci. Am. Stat. Soc., 4730-4736, (2003)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.