[01] Ahmed Majeed Hameed, Al-Safwa University College, Department of Computer Technics, Karbala, Iraq. [02] Moaz H. Ali, Al-Safwa University College, Department of Computer Technics, Karbala, Iraq. [03] Ramla Abdulnabi Abdulzahra, Al-Safwa University College, Department of Computer Technics, Karbala, Iraq.

Quality measurement is the process of measuring distortion in images, by using some metrics that makes a comparison between the original pure image and the distorted image. Image quality measurement is important and helpful for many applications such as in medicine and space images because images can be affected with many factors of distortions. It is used the Normalize Mean Square Error (NMSE) and the Structural Similarity Index Measurement (SSIM) as a metric to measure the quality of distorted images. Gaussian blurring is the type of distortion which is used, so this distortion is applied manually on four color images using Gaussian blurring function. The distortion is applied on images in the red, green, blue (RGB) and Hue, Saturation, Value (HSV) color spaces. The result is shown that in the (HSV) the achromatic components have been affected strongly by blurring than chromatic components, but in the (RGB) colors and lightness are affected similarly because of the high interdependence between lightness and colors in RGB color space. Experimental results show that in HSV color space there is a high separation between chromatic and achromatic components, where achromatic component has been affected strongly with blur distortion than chromatic components. Also, results of RGB shows a high correlation between chromatic and achromatic components, where these components were identically affected with blur distortion.

Blurring, NMSE, SSIM, HSV, RGB, Gaussian Blurring, Image Quality

[01] F. Kerouh, and A. Serir, “A No Reference Quality Metric for Measuring Image Blur In Wavelet Domain” International journal of digital information and wireless communication, pp. 767-776, 2011. [02] C.Sasi varnan, A.Jagan, Jaspreet Kaur, Divya Jyoti and Dr. D.S.Rao, “Image Quality Assessment Techniques pn Spatial Domain”, pp. 177-184, 2011. [03] R. Kreis, “Issues of spectral quality in clinical H-magnetic resonance spectroscopy and a gallery of artifacts”, NMR in Biomedecine, vol. 17, no. 6, pp. 361-381, 2004. [04] I. Avcibas, B. Sankur and K. Sayood, “Statistical evaluation of image quality measures”, Journal of Electronic Imaging, vol. 11, no. 2, pp. 206-223, 2002. [05] J. E. Farrell, "Image quality evaluation in color imaging: vision and technology", L.W. MacDonald, and M.R. Luo, Wiley press, pp. 285-313, 1999. [06] M. Cadik and P. Slavik, “Evaluation of two principal approaches to objective image quality assessment”, 8th International Conference on Information Visualisation, IEEE Computer Society Press, pp. 513-551, 2004. [07] T. B. Nguyen and D. Ziou, “Contextual and non-contextual performance evaluation of edge detectors”, Pattern Recognition Letters, vol. 21, no.9, pp. 805-816, 2000. [08] O. Elbadawy, M. R. El-Sakka, and M. S. Kamel, “An information theoretic image-quality measure”, Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, vol. 1, pp. 169-172, 1998. [09] A. Medda and V. Debrunner, “Color image quality index based on the UIQI”, Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 213-217, 2006. [10] H. Abbas. "Color image processing", M.SC. Thesis, Computer Engineering Dept. Collage of Engineering Baghdad University, 1997. [11] Mireille Sendashonga and Fabrice Labeau, “Low Complexity Image Quality Assessment Using Frequency Domain Transforms”, Centre for Advanced Systems & Technologies in Communications (SYTACom), 1-4244-0481, 385-388, 2006. [12] Sonia Ouni, Ezzeddine Zagrouba and Majed Chambah, “A New No-reference Method for Color Image Quality Assessment”, International Journal of Computer Applications (0975 – 8887) Volume 40– No.17, pp. 24-31, 2012. [13] Alexandre Ciancio, André Luiz N. Targino da Costa, Eduardo A. B. da Silva, Senior Member, IEEE, Amir Said, Ramin Samadani, and Pere Obrador, “No-Reference Blur Assessment of Digital Pictures Based on Multifeature Classifiers”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 1, pp. 64-75, 2011. [14] Haim Levkowitz, “Color theory and modeling for computer graphics, visualization, and multimedia applications”, Kluwer Academic Publishers, 1997. [15] Roger Bourne, “Fundamentals of Digital Imaging in Medicine", Springer, ISBN 978-1-84882-086-9, 2010. [16] Marc Ebner, "Color Constancy", John Wiley & Sons, 2007. [17] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, "Digital Image Processing using Matlap", By Pearson Education Inc., 2004. [18] A. M. Eskicioglu and P. S. Fisher, “Image quality measures and their performance”, IEEE Trans. Communication, vol. 43, pp. 2959–2965, Dec. 1995. [19] Z. Wang and A. C. Bovik, “A universal image quality index”, IEEE Signal Processing Letters, vol. 9, pp. 81–84, Mar. 2002. [20] Z. Wang, Eero P. Simoncelli, Howard Hughes, "Local Phase Coherence and the Perception of Blur in: Adv. Neural Information Processing Systems". pp. 786-792. 2003 [21] D. J. Jabson, Z. Rahman, G. A. Woodell, “Retinex processing for automatic image enhancement”, Journal of Electronic Imaging, Vol. 13(1), PP.100–110, January 2004. [22] Rafael C. Gonzales, Richard E. Woods, "Digital Image Processing”, second edition, Prentice Hall, 2002. [23] Yusra A. Y. Al-Najjar, Dr. Der Chen Soong, "Comparison of Image Quality Assessment: PSNR, HVS, SSIM, UIQI", International Journal of Scientific & Engineering Research, Volume 3, Issue 8, August-2012 1 ISSN 2229-5518. [24] W. S. Malpica, A. C. Bovik," SSIM based range image quality assessment" Fourth International workshop on Video Processing and Quality Metrics for Consumer Electronics Scottsdale Arizon, 2009.