Abstract
We describe a method for objective and quantitative evaluation of image quality. The method represents a novel use of image enhancement concepts. It employs three new measures that evaluate the definition of contours, uniform intensity distribution, and noise rate in determining the image quality. Because the three measures have clear physical meanings, they can be selectively applied according to the viewer’s evaluation criteria. The three measures are relatively inexpensive to compute, making them suitable for automated ranking of image quality in personal digital imaging devices, such as digital cameras. However, the method is equally adept at evaluating other digital images such as those on the Internet. Experiments with the method show good correlation with visual quality assessment for various image subject types.
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References
Otaki, N.: Colour Image Evaluation Systems. OKI Technical Review, Issue 194, vol.70(2), pp. 68-73 (2003)
Velecký, P., PospÃsil, J.: Digital Image Evaluation of Metallographic Microstructure of Sintered Carbides. Acta Univ. Palacki. Olomuc., Fac. Rer. Nat., Physica 38, 105–113 (1999)
Wilhjelm, J.E., Jensen, M.S., Jespersen, S.K., Sahl, B., Falk, E.: Visual and quantitative evaluation of selected image combination schemes in ultrasound spatial compound scanning. IEEE Transactions on Medical Imaging 23(2), 181–190 (2004) ISSN: 0278-0062
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Walter, I.M., Lockemann, P.C., Nagel, H.H.: Database Support for Knowledge-Based Image Evaluation. In: ACM Proceedings of the 13th International Conference on Very Large Data Bases, September 1-4, pp. 3–11 (1987)
Eckstein, M.P., Bartroff, J.L., Abbey, C.K., Whiting, J.S., Bochud, F.O.: Automated computer evaluation and optimization of image compression of x-ray coronary angiograms for signal known exactly detection tasks. Optics Express, University of California, Santa Barbara, March 10,  11(5), 460–475 (2003)
Saghri, J.A., Cheatham, P.S., Habibi, A.: Image Quality Measure Based on a Human Visual System Model. Optical Engineering 28(7), 813–818 (1989)
Nill, N.B., Bouzas, B.H.: Objective Image Quality Measure Derived from Digital Image Power Spectra. Optical Engineering 31(4), 813–825 (1992)
Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
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Yao, H., Huseh, MY., Yao, G., Liu, Y. (2005). Image Evaluation Factors. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_32
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DOI: https://doi.org/10.1007/11559573_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29069-8
Online ISBN: 978-3-540-31938-2
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