Abstract
Available color images can be encoded in any color space. However, according to the image display model, it is assumed that the color image is encoded in a specific color space belonging to the RGB family. Displaying an encoded image in a color space by using a system designed for the display of encoded images in another color space leads to a poor reproduction of colors. To overcome this problem, the encoded image in a color space must be converted to the color space used by the display system. Unfortunately, the image color space can be not included in the image metadata and therefore it is unknown. Even if the display systems are massively used, this issue does not seem to be tackled before. In this paper, we propose the identification of the image color space from its colors. First, the Gamut of color spaces is estimated by using a collection of images. Then, the image color histogram is compared to the estimated Gamuts. The obtained identification scores using four color spaces and a collection of 2106 images are encouraging.
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References
Cotton, S.D.: Colour, Colour Spaces, and the Human Visual System. Technical report, School of computer science, University of Birmingham, England (1995)
Süsstrunk, S., Holm, J., Finlayson, G.D.: Chromatic Adaptation Performance of Different RGB Sensors. In: IS&T/SPIE Electronic Imaging, vol. 4300, pp. 172–183 (2001)
Ziou, D., Lahmar, K.N.: Content Based Computational Chromatic Adaptation, Technical report, Dept. Informatique, Universit de Sherbrooke (2014)
Schwarz, M.W., Cowan, W.B., Beatty, J.C.: An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models. ACM Trans. Graph. 6, 123–158 (1987)
Terrillon, J.-C., Akamatsu, S.: Comparative performance of different chrominance spaces for color segmentation and detection of human faces in complex scene images. In: Conference on Vision Interface (VI 1999), pp. 180–187 (2000)
Albiol, A., Torres, L., Delp, E.J.: Optimum color spaces for skin detection. In: ICIP, vol. 1, pp. 122–124 (2001)
Busin, L., Vandenbroucke, N., Macaire, L.: Color spaces and image segmentation. Adv. Imaging Electron Phys. 151, 65–168 (2008)
Süsstrunk, S., Buckley, R., Swen, S.: Standard RGB color spaces. In: Color Imaging Conference: Color Science, Systems, and Applications, pp. 127–134 (1999)
Neagoe, V.: An optimum 2D color space for pattern recognition. In: Image Processing Computer Vision Pattern Recognition, pp. 526–532 (2006)
Filali, I., Ziou, D., Benblidia, N.: Multinomial bayesian kernel logistic discriminant based method for skin detection. In: SITIS, pp. 420–425 (2012)
Finlayson, G.D., Drew, M.S., Funt, B.V.: Spectral sharpening: sensor transformations for improved color constancy. JOSA A 11, 1553–1563 (1994)
Finlayson, G.D., Hordley, S.D., Hubel, P.M.: Color by correlation: a simple, unifying framework for color constancy. IEEE TPAMI 23, 1209–1221 (2001)
Morovic, J., Luo, M.R.: Calculating medium and image gamut boundaries for gamut mapping. Color Res. Appl. 25, 394–401 (2000)
Terrades, O.R., Valveny, E., Tabbone, S.: Optimal classifier fusion in a non-bayesian probabilistic framework. IEEE TPAMI 31, 1630–1644 (2009)
Lee, S.M., Xin, J.H., Westland, S.: Evaluation of image similarity by histogram intersection. Color Res. Appl. 30(4), 265–274 (2005)
Madi, A., Ziou, D.: Color constancy for visual compensation of projector displayed image. Displays 36, 6–17 (2014)
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© 2015 Springer International Publishing Switzerland
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Vezina, M., Ziou, D., Kerouh, F. (2015). Color Space Identification for Image Display. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_51
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DOI: https://doi.org/10.1007/978-3-319-20801-5_51
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