Abstract
This paper provides an alternative framework for color-to-grayscale image conversion by exploiting the chrominance information present in the color image using singular value decomposition (SVD). In the proposed technique of color-to-grayscale image conversion, a weight matrix corresponds to the chrominance components is derived by reconstructing the chrominance data matrix (planes a* and b*) from the eigenvalues and eigenvectors computed using SVD. The final grayscale converted image is obtained by adding the weighted chrominance data to the luminous intensity which is kept intact for the CIEL*a*b* color space of the given color image. The effectiveness of the proposed grayscale conversion is confirmed by the comparative analysis performed on the color-to-gray benchmark dataset across 10 existing algorithms based on the standard objective measures, namely normalized cross-correlation, color contrast preservation ratio, color content fidelity ratio, E score and subjective evaluation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Gooch, Amy A., Olsen, Sven C., Tumblin, J., Gooch, B.: Color2gray: salience-preserving color removal. In: ACM Transaction on Graphics(TOG) in Proceedings of ACM SIGGRAPH 2005, vol. 24, no. 3, pp. 634–639 (2005)
CewuLu, LiXu, Jia, J.: Contrast preserving decolorization. In: Proceedings of the IEEE International Conference. Computational Photography (ICCP), pp. 1–7 (2012)
Grundland, M., Dodgson, N.A.: Decolorize: Fast, contrast enhancing, color to grayscale conversion. Pattern Recognit. 40(11), 2891–2896 (2007)
Kim, Y., Jang, C., Demouth, J., Lee, S.: Robust color-to-gray via nonlinear global mapping. ACM Trans. on Graphics (SIGGRAPH ASIA ’09) 28(5), 161–164 (2009)
Liu, C.W., Liu, T.L.: A sparse linear model for saliency-guided decolorization. In: Proceedings of the 20th IEEE International Conference Image Processing (ICIP), pp. 1105–1109 (2013)
Lu, C., Xu, L., Jia, J.: Real-time contrast preserving decolorization. In: Proceedings of the International Conference Computer Graphics and Interactive Techniques (SIGGRAPH’12), pp. 34:1–34:4 (2012)
Lu, C., Xu, L., Jia, J.: Contrast preserving decolorization with perception-based quality metrics. Int. J. Comput. Vis. 110(2), 222–239 (2014)
Cadik, M.: Perceptual evaluation of color-to-grayscale image conversions. Comput. Gr. Forum 27(7), 1745–1754 (2008)
Neumann, L., M.Cadik, Nemcsics, A.: An efficient perception-based adaptive color to gray transformation. In: Proceedings of the 3rd Eurographics conference Computational Aesthetics in Graphics, Visualization and Imaging, pp. 73–80 (2007)
Rasche, K., Geist, R., Westall, J.: Re-coloring images for gamuts of lower dimension. Comput. Gr. Forum 24(3), 423–432 (2005)
Suhre, A., Kose, K., Cetin, A., N.Gurcan, M.: Content-adaptive color transform for image compression. In: Proceedings of the 17th International Conference Image Processing (2010)
Wu, T., Toet, A.: Color-to-grayscale conversion through weighted multiresolution channel fusion. J. Electron. Imaging 23(4), 1–6 (2014)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sowmya, V., Govind, D. & Soman, K.P. Significance of incorporating chrominance information for effective color-to-grayscale image conversion. SIViP 11, 129–136 (2017). https://doi.org/10.1007/s11760-016-0911-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-016-0911-8