Abstract
The purpose of this paper is to introduce a new method for image quality assessment (IQA). The method adopted here is assumed to be Full-reference measure. Color images that are corrupted with different kinds of distortions are assessed by applying a color distorted algorithm on each color component separately. This approach use especially YIQ color space in computation. Gradient operator was successfully introduced to compute gradient image from the luminance channel of images. In this paper, we propose an alternative technique to evaluate image quality. The main difference between the new proposed method and the gradient magnitude similarity deviation (GMSD) method is the usage of color component for the detection of distortion.
Experimental comparisons demonstrate the effectiveness of the proposed method.
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Yang, C., Kwok, S.H.: Efficient gamut clipping for color image processing using LHS and YIQ. Opt. Eng. 42(3), 701–711 (2003)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simocelli, E.P.: Image quality assessment: From error measurement to structural similarity. IEEE Trans. Image Processing 13(4), 600–612 (2004)
Guan-Hao, C., Chun-Ling, Y., Sheng-Li, X.: Gradient-based structural similarity for image quality assessment. In: Proc. ICIP 2006, pp. 2929–2932 (2006)
Ahmed Seghir, Z., Hachouf, F.: Edge-region information measure based on deformed and displaced pixel for Image Quality Assessment. Signal Processing: Image Communication 26(8-9), 534–549 (2011)
Final VQEG report on the validation of objective quality metrics for video quality assessment: http://www.its.bldrdoc.gov/vqeg/projects/frtv_phaseI/
Zhang, F., Ma, L., Li, S.: Practical image quality metric applied to image coding. IEEE Trans. Multimedia 13, 615–624 (2011)
Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision. McGraw-Hill, NewYork (1995)
Jähne, B., Haubecker, H., Geibler, P.: Handbook of Computer Vision and Applications. Academic, New York (1999)
Ponomarenko, N., Egiazarian, K.: Tampere Image Database, TID 2008, http://www.ponomarenko.info/tid2008.htm
Larson, C., Chandler, D.M.: Categorical Image Quality (CSIQ) Database 2009, http://vision.okstate.edu/csiq
Sheikh, H.R., Seshadrinathan, K., Moorthy, A.K., Wang, Z., Bovik, A.C., Cormack, L.K.: Image and Video Quality Assessment Research at LIVE 2004 (2004), http://live.ece.utexas.edu/research/quality
Ponomarenko, N., et al.: Color image database TID2013: Peculiarities and preliminary results. In: Proc. 4th Eur. Workshop Vis. Inf. Process., pp. 106–111 (June 2013)
Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 1–26 (2011)
Xue, W., Zhang, L., Mou, X., Bovik, A.C.: Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index. Presented at IEEE Transactions on Image Processing, 684–695 (2014)
Kovesi, P.: Image features from phase congruency. Videre: Journal of Computer Vision Research 1(3), 1–26 (1999)
Gaubatz, M.: Metrix MUX Visual Quality Assessment Package: MSE, PSNR, SSIM, MSSIM, VSNR, VIF, VIFP, UQI, IFC, NQM, WSNR, SNR
Liu, A., Lin, W., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing 21(4), 1500–1512 (2012)
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: Proc. IEEE Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, pp. 1398–1402 (November 2003)
Chandler, D.M., Hemami, S.S.: VSNR: A wavelet-based visual signal-to-noise-ratio for natural images. IEEE Trans. Image Process. 16(9), 2284–2298 (2007)
Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)
Sheikh, H.R., Bovik, A.C., de Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. on Image Processing 14(12), 2117–2128 (2005)
Damera-Venkata, N., Kite, T.D., Geisler, W.S., Evans, B.L., Bovik, A.C.: Image quality assessment based on degradation model. IEEE Trans. on Image Processing 9(4), 636–650 (2000)
Larson, E.C., Chandler, D.M.: Most apparent distortion: Full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1), 011006:1–011006:21 (2010)
Larson, E., Chandler, D.: Full-Reference Image Quality Assessment and the Role of Strategy: The Most Apparent Distortion, http://vision.okstate.edu/mad/
Chok, N.S.: Pearson’s Versus Spearman’s and Kendall’s Correlation Coefficients for Continuous Data. Master’s Thesis, University of Pittsburgh (2010)
Wang, Z., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Trans. Image Process. 20(5), 1185–1198 (2011)
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Seghir, Z.A., Hachouf, F. (2015). Full-Reference Image Quality Assessment Measure Based on Color Distortion. In: Amine, A., Bellatreche, L., Elberrichi, Z., Neuhold, E., Wrembel, R. (eds) Computer Science and Its Applications. CIIA 2015. IFIP Advances in Information and Communication Technology, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-19578-0_6
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DOI: https://doi.org/10.1007/978-3-319-19578-0_6
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