Abstract
Since most of even recently proposed image quality assessment metrics are typically applied for a single color channel in both compared images, a reliable color image quality assessment is still a challenging task for researchers. One of the major drawbacks limiting the progress in this field is the lack of image datasets containing the subjective scores for images contaminated by color specific distortions. After the publication of the TID2013 dataset, containing i.a. images with 6 types of color distortions, this situation has changed, however there is still a need of validation of some recently proposed grayscale metrics in view of their applicability for color specific distortions.
In this paper some results obtained using different approaches to color to grayscale conversion for some well-known metrics as well as for recently proposed combined ones, are presented and discussed, leading to meaningful increase of the prediction accuracy of image quality for color distortions.
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References
Čadík, M.: Perceptual evaluation of color-to-grayscale image conversions. Comput. Graph. Forum 27(7), 1745–1754 (2008)
Gooch, A.A., Olsen, S.C., Tumblin, J., Gooch, B.B.: Color2gray: salience-preserving color removal. ACM Transactions on Graphics 24(3), 634–639 (2005)
Grundland, M., Dodgson, N.A.: Decolorize: Fast, contrast enhancing, color to grayscale conversion. Pattern Recognition 40(11), 2891–2896 (2007)
International Telecommunication Union: Recommendation P.910 - Subjective video quality assessment methods for multimedia applications (1999)
International Telecommunication Union: Recommendation BT.709-5 - Parameter values for the HDTV standards for production and international programme exchange (2002)
International Telecommunication Union: Recommendation BT.601-7 - Studio encoding parameters of digital television for standard 4:3 and wide-screen 16:9 aspect ratios (2011)
Kanan, C., Cottrell, G.W.: Color-to-grayscale: Does the method matter in image recognition? PLOS One 7(1), e29740 (2012)
Liu, T.J., Lin, W., Kuo, C.C.J.: Image quality assessment using multi-method fusion. IEEE Trans. Image Processing 22(5), 1793–1807 (2013)
Mansouri, A., Mahmoudi-Aznaveh, A., Torkamani-Azar, F., Jahanshahi, J.: Image quality assessment using the Singular Value Decomposition theorem. Optical Review 16(2), 49–53 (2009)
Okarma, K.: Combined Image Similarity Index. Optical Review 19(5), 349–354 (2012)
Okarma, K.: Extended hybrid image similarity – combined full-reference image quality metric linearly correlated with subjective scores. Elektronika Ir Elektrotechnika 19(10), 129–132 (2013)
Okarma, K.: Combined full-reference image quality metric linearly correlated with subjective assessment. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part I. LNCS, vol. 6113, pp. 539–546. Springer, Heidelberg (2010)
Okarma, K.: Hybrid feature similarity approach to full-reference image quality assessment. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 212–219. Springer, Heidelberg (2012)
Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID2008 - a database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics 10, 30–45 (2009)
Ponomarenko, N., Ieremeiev, O., Lukin, V., Jin, L., Egiazarian, K., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., Kuo, C.C.J.: Color image database TID2013: Peculiarities and preliminary results. In: Proc. 4th European Workshop on Visual Information Processing, EUVIP 2013, Paris, France, pp. 106–111 (2013)
Ponomarenko, N., et al.: A new color image database TID2013: Innovations and results. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2013. LNCS, vol. 8192, pp. 402–413. Springer, Heidelberg (2013)
Sheikh, H., Bovik, A.: Image information and visual quality. IEEE Transactions on Image Processing 15(2), 430–444 (2006)
Tourancheau, S., Autrusseau, F., Sazzad, Z., Horita, Y.: Impact of subjective dataset on the performance of image quality metrics. In: Proc. 15th IEEE Int. Conf. Image Processing, San Diego, California, pp. 365–368 (2008)
Wang, Z., Simoncelli, E., Bovik, A.: Multi-Scale Structural Similarity for image quality assessment. In: Proc. 37th IEEE Asilomar Conf. Signals, Systems and Computers, Pacific Grove, California (2003)
Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: A Feature Similarity index for image quality assessment. IEEE Trans. Image Processing 20(8), 2378–2386 (2011)
Zhang, L., Zhang, L., Mou, X.: RFSIM: A feature based image quality assessment metric using Riesz transforms. In: Proc. 17th IEEE Int. Conf. Image Processing, Hong Kong, China, pp. 321–324 (2010)
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Okarma, K. (2014). A Validation of Combined Metrics for Color Image Quality Assessment. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_1
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DOI: https://doi.org/10.1007/978-3-319-11331-9_1
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