Abstract
An opportunity of using self-similarity in evaluation of image visual quality is considered. A method for estimating self-similarity for a given image fragment that takes into account contrast sensitivity function is proposed. Analytical expressions for describing the proposed parameter distribution are derived, and their importance to human vision system based image visual quality full-reference evaluation is proven. A corresponding metric is calculated and a mean squared difference for the considered parameter maps in distorted and reference images is considered. Correlation between this metric and mean opinion score (MOS) for five largest openly available specialized image databases is calculated. It is demonstrated that the proposed metric provides a correlation at the level of the best known metrics of visual quality. This, in turn, shows an importance of fragment self-similarity in image perception.
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References
Keelan, B.W.: Handbook of Image Quality. Marcel Dekker, Inc., New York (2002)
Ponomarenko, N., Krivenko, S., Lukin, V., Egiazarian, K.: Lossy Compression of Noisy Images Based on Visual Quality: A Comprehensive Study. EURASIP Journal on Advances in Signal Processing, 13 (2010), doi:10.1155/2010/976436
Carli, M.: Perceptual Aspects in Data Hiding. Thesis for the degree of Doctor of Technology, Tampere University of Technology (2008)
Fevralev, D., Lukin, V., Ponomarenko, N., Abramov, S., Egiazarian, K., Astola, J.: Efficiency analysis of DCT-based filters for color image database. In: Proceedings of SPIE Conference Image Processing: Algorithms and Systems VII, San Francisco, vol. 7870 (2011)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)
Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID2008 - A Database for Evaluation of Full-Reference Visual Quality Assessment Metrics. In: Advances of Modern Radioelectronics, vol. 10, pp. 30–45 (2009)
Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms. IEEE Transactions on Image Processing 15(11), 3441–3452 (2006)
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: IEEE Asilomar Conference on Signals, Systems and Computers, pp. 1398–1402 (2003)
Watson, A.B., Solomon, J.A.: Model of visual contrast gain control and pattern masking. J. Opt. Soc. Am. A 14(9), 2379–2391 (1997)
Sheikh, H.R., Bovik, A.C., Cormack, L.K.: No-reference quality assessment using natural scene statistics: JPEG2000. IEEE Transactions on Image Processing 14(11), 1918–1927 (2005)
Ponomarenko, N., Eremeev, O., Egiazarian, K., Lukin, V.: Statistical evaluation of no-reference image visual quality metrics. In: Proceedings of EUVIP, Paris, p. 5 (2010)
Ponomarenko, N., Silvestri, F., Egiazarian, K., Carli, M., Astola, J., Lukin, V.: On between-coefficient contrast masking of DCT basis functions. In: Proc. of the Third International Workshop on Video Processing and Quality Metrics, USA, p. 4 (2007)
Wallace, G.K.: The JPEG Still Picture Compression Standard. Comm. of the ACM 34, 30–44 (1991)
Ponomarenko, N.N., Lukin, V.V., Egiazarian, K.O., Astola, J.T.: A method for blind estimation of spatially correlated noise characteristics. In: Proceedings of SPIE Conference Image Processing: Algorithms and Systems VII, San Jose, p. 12 (2010)
Arnold, B.C., Balakrishnan, N., Nagaraja, H.N.: A First Course in Order Statistics. A Wiley-Interscience Publication, NY (1992)
Mannos, J.L., Sakrison, D.J.: The Effects of a Visual Fidelity Criterion on the Encoding of Images. IEEE Transactions on Information Theory 20(4), 525–535 (1974)
MSDDM metric page, http://ponomarenko.info/msddm.htm
Ninassi, A., Le Callet, P., Autrusseau, F.: Pseudo No Reference image quality metric using perceptual data hiding. In: SPIE Human Vision and Electronic Imaging, San Jose, vol. 6057-08 (2006)
Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging 19(1), 011006 (2010)
Horita, Y., Kawayoke, Y., Parvez Sazzad, Z.M.: Image quality evaluation database (2011), http://160.26.142.130/toyama_database.zip
Kendall, M.G.: The advanced theory of statistics, vol. 1, p. 457. Charles Griffin & Company limited, London (1945)
Wang, Z., Bovik, A.: A universal image quality index. IEEE Signal Processing Letters 9, 81–84 (2002)
Damera-Venkata, N., Kite, T., Geisler, W., Evans, B., Bovik, A.: Image Quality Assessment Based on a Degradation Model. IEEE Transactions on Image Processing 9, 636–650 (2000)
Murthy, A.V., Karam, L.J.: A MATLAB Based Framework For Image and Video Quality Evaluation. In: International Workshop on Quality of Multimedia Experience (QoMEX), pp. 242–247 (2010)
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Ponomarenko, N., Jin, L., Lukin, V., Egiazarian, K. (2011). Self-Similarity Measure for Assessment of Image Visual Quality. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_42
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DOI: https://doi.org/10.1007/978-3-642-23687-7_42
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