Abstract
Color comparison is a key aspect in many areas of application, including industrial applications, and different metrics have been proposed. In many applications, this comparison is required to be closely related to human perception of color differences, thus adding complexity to the process. To tackle this, different approaches were proposed through the years, culminating in the CIEDE2000 formulation. In our previous work, we showed that simple color properties could be used to reduce the computational time of a color similarity decision process that employed this metric, which is recognized as having high computational complexity. In this paper, we show mathematically and experimentally that these findings can be adapted and extended to the recently proposed CIEDE2000 PF metric, which has been recommended by the CIE for industrial applications. Moreover, we propose new efficient models that not only achieve lower error rates, but also outperform the results obtained for the CIEDE2000 metric.
Similar content being viewed by others
References
Ancuti, C.O., Ancuti, C., Sbert, M., Timofte, R.: Dense haze: a benchmark for image dehazing with dense-haze and haze-free images. arXiv preprint arXiv:1904.02904 (2019)
Blaznik, B., Bračko, S.: Study of ink jet print resistance using various colour difference formulas. Tehnički vjesnik 26(1), 243–247 (2019)
Czigola, A., Abram, E., Kovacs, Z.I., Marton, K., Hermann, P., Borbely, J.: Effects of substrate, ceramic thickness, translucency, and cement shade on the color of CAD/CAM lithium-disilicate crowns. J. Esthet. Restor. Dent. 31(5), 457–464 (2019)
Refsnes Data. Color Names Supported by All Browsers. Accessed 3 June 2020
D’Orazio, T., Leo, M., Mosca, N., Spagnolo, P., Mazzeo, P.L.: A semi-automatic system for ground truth generation of soccer video sequences. In: 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 559–564. IEEE (2009)
Fisher, R. Caviar dataset (2004)
Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)
Griffin, G., Holub, A., Perona P.: Caltech-256 object category dataset. CalTech Report, 03 (2007)
Huang, M., Cui, G., Melgosa, M., Sánchez-Maranón, M., Li, C., Luo, M.R., Liu, H.: Power functions improving the performance of color-difference formulas. Opt. Express 23(1), 597–610 (2015)
Kang, H.R.: Computational Color Technology. Spie Press, Bellingham (2006)
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al.: The open images dataset v4. Int. J. Comput. Vis. 18, 1–26 (2020)
Luo, M.R., Cui, G., Rigg, B.: The development of the CIE 2000 colour-difference formula: Ciede 2000. Color Res. Appl. 26(5), 340–350 (2001)
McDonald, R., Smith, K.J.: Cie94-a new colour-difference formula. J. Soc. Dyers Colour. 111(12), 376–379 (1995)
Montiel, J., Mitchell, R., Frank, E., Pfahringer, B., Abdessalem, T., Bifet A.: Adaptive XGBoost for evolving data streams. arXiv preprint arXiv:2005.07353 (2020)
Murillo, M.A., Rodríguez-Pulido, F.J., Heredia, F.J., Melgosa, M., Pacheco, J., Vargas, R., Montero, E., Gutiérrez, D.: Color evolution during a coating process of pharmaceutical tablet cores by random spraying. Color Res. Appl. 44(2), 160–167 (2019)
Nene, S.A., Nayar, S.K., Murase, H.: Object image library (coil-100) (1996)
Ouyang, W., Xu, B., Yuan, X.: Color segmentation in multicolor images using node-growing self-organizing map. Color Res. Appl. 44(2), 184–193 (2019)
Paravina, R., Sanchez, N.P., Ghinea, R.I., Powers, J.: Colorimetric (ciede2000) comparison between two shade guides used for visual evaluation of tooth whitening efficacy. Srpski arhiv za celokupno lekarstvo 147, 6 (2019)
Pereira, A., Carvalho, P., Coelho, G., Côrte-Real, L.: Efficient ciede2000-based color similarity decision for computer vision. IEEE Trans. Circuits Syst. Video Technol. 56, 1 (2019)
Pérez, M.M., Herrera, L.J., Carrillo, F., Pecho, O.E., Dudea, D., Gasparik, C., Ghinea, R., Della Bona, A.: Whiteness difference thresholds in dentistry. Dent. Mater. 35(2), 292–297 (2019)
Richter, K., Bračko, S., Cui, G., Luo, M.R., Melgosa, M., Seim, T.: Validity of formulae for predicting small colour differences. Technical report, CIE, International Commision on Illumination. CIE 230:2019 (2019)
Robertson, Alan R.: The CIE 1976 color-difference formulae. Color Res. Appl. 2(1), 7–11 (1977)
Viana, P., Carvalho, P., Andrade, M.T., Jonker, P.P., Papanikolaou, V., Teixeira, I.N., Vilaça, L., Pinto, J.P., Costa, T.: Semantic storytelling automation: a context-aware and metadata-driven approach. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 4491–4493 (2020)
Wu, H.-T., Wu, Y., Guan, Z., Cheung, Y.: Lossless contrast enhancement of color images with reversible data hiding. Entropy 21(9), 910 (2019)
Yang, Y., Ming, J., Yu, N.: Color image quality assessment based on ciede2000. Adv. Multimedia 2012, 11 (2012)
Young, D.P., Ferryman, J.M.: Pets metrics: on-line performance evaluation service. In: 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 317–324. IEEE (2005)
Acknowledgements
This work was partially funded by the project FotoInMotion (GA: 780612) funded by H2020 Framework Programme of the European Commission and also by Fundação para a Ciência e Tecnologia (FCT) with PhD Grant SFRH/BD/146400/2019.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pereira, A., Carvalho, P. & Côrte-Real, L. Boosting color similarity decisions using the CIEDE2000_PF Metric. SIViP 16, 1877–1884 (2022). https://doi.org/10.1007/s11760-022-02147-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-022-02147-w