Abstract
The RGB2GRAY conversion model is the classical and most popularly used tool for image decolorization. Recent researches have validated that optimally selecting the three weighting parameters in this first-order linear model has great potential to improve its conversion ability. A question is naturally raised that extending the parameter range will count for further improvement? In this paper, we present a simple yet efficient strategy to extend the parameter range for achieving such goal. In the extended model, the parameter range is extended to be [−1, 1] and the sum of three parameters is still constrained to be 1. A discrete searching solver is proposed by determining the solution with the minimum function value from the linear parametric model induced candidate images. Among the solving procedure, the newly presented vector p-norm of gradient correlation similarity measure is utilized. Extensive experiments under a variety of test images and a comprehensive evaluation against the state-of-the-art methods consistently demonstrate the potential of the proposed model and algorithm.
Similar content being viewed by others
References
Ancuti CO, Ancuti C, Bekaert P (2011) Enhancing by saliency guided decolorization. In: Proceedings of IEEE conference CVPR, p 257–264
Bala R, Eschbach R (2004) Spatial color-to-grayscale transform preserving chrominance edge information. In Color Imaging Conference, p 82–86
ByeongJu L, Jongwon C, Yun K et al (2015) Gradient preserving RGB-to-gray conversion using random forest. In: IEEE international conference on proceedings of the image processing (ICIP), p 3170–3174
Cadik M (2008) Perceptual evaluation of color-to-grayscale image conversions. Comput Graph Forum 27(7):1745–1754
Du H, He S, Sheng B, et al. (2015) Saliency-guided color-to-gray conversion using region-based optimization. IEEE Trans Image Process 24(1):434–443
Eynard D, Kovnatsky A, Bronstein MM (2014) Laplacian colormaps: a framework for structure-preserving color transformations. In: Proceedings of the computer graphics forum. Wiley Online Library 33(2):215–224
Gooch A, Olsen SC, Tumblin J, Gooch B (2005) Color2gray: salience-preserving color removal. ACM Trans Graph 24(3):634–639
Grundland M, Dodgson NA (2007) Decolorize: fast, contrast enhancing, color to grayscale conversion. Pattern Recogn 40(11):2891–2896
Ji Z, Fang M, Wang Y et al (2015) Efficient decolorization preserving dominant distinctions. The Visual Computer, p 1–11
Jin Z, Li F, Ng MK (2014) Avariational approach for image decolorization by variance maximization. SIAM J Imaging Sci 7(2):944–968
Kim Y, Jang C, Demouth J, Lee S (2009) Robust color-to gray via nonlinear global mapping. ACM Trans Graph 28(5):1–4
Kuk JG, Ahn JH, Cho NI (2010) A color to grayscale conversion considering local and global contrast. Proc ACCV 4:513–524
Liu Q, Liu J, Dong P, Liang D (2013) SGTD: Structure gradient and texture decorrelating regularization for image decomposition. In: Proceedings of conference ICCV, p 1081–1088
Liu Q, Liu PX, Xie W, Wang Y, Liang D (2015) GcsDecolor: gradient correlation similarity for efficient contrast preserving decolorization. IEEE Trans Image Process 23(5):2889–2904
Lu C, Xu L, Jia J (2012a) Contrast preserving decolorization. In: IEEE international conference on computational photography (ICCP), p 1–7
Lu C, Xu L, Jia J (2012b) Real-time contrast preserving decolorization. ACM Siggraph Asia Technical Berief
Lu C, Xu L, Jia J (2014) Contrast preserving decolorization with perception-based quality metrics. Int J Comput Vis 110:222–239
Neumann L, Cadik M, Nemcsics A (2007) An efficient perception-based adaptive color to gray transformation. In: Computational aesthetics, p 73–80
Rasche K, Geist R, Westall J (2005) Re-coloring images for gamuts of lower dimension. Comput Graph Forum 24(3):423–432
Smith K, Landes PE, Thollot J, Myszkowski K (2008) Apparent greyscale: a simple and fast conversion to perceptually accurate images and video. Comput Graph Forum 27(2):193–200
Song M, Tao D, Chen C, Li X, Chen CW (2010) Color to gray: visual cue preservation. IEEE Trans Pattern Anal Mach Intell 32(9):1537–1552
Song M, Tao D, Bu J, Chen C, Yang Y (2013a) Color-to-gray based on chance of happening preservation. Neurocomputing 119:222–231
Song Y, Bao L, Xu X, Yang Q (2013) Decolorization: is rgb2gray () out? ACM Siggraph Asia Technical Briefs
Song Y, Bao L, Yang Q (2014) Real-time video decolorization using bilateral filtering. In: IEEE winter conference on proceedings of the applications of computer vision (WACV), p 159–166
Xue W, Zhang L, Mou X, Bovik AC (2014) Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans Image Process 23(2):684–695
Acknowledgments
The authors sincerely thank the anonymous reviewers for their valuable comments and constructive suggestions that are very helpful in the improvement of this paper. This work was partly supported by the National Natural Science Foundation of China under 61362001, 61503176, 61261010, 51165033, the Natural Science Foundation of Jiangxi Province under 20151BAB207008, 20151BAB207007, Jiangxi Advanced Projects for Post-doctoral Research Funds under 2014KY02 and the international postdoctoral exchange fellowship program.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, Q., Xiong, J., Zhu, L. et al. Extended RGB2Gray conversion model for efficient contrast preserving decolorization. Multimed Tools Appl 76, 14055–14074 (2017). https://doi.org/10.1007/s11042-016-3748-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-016-3748-9