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Estimating the amount of cyan, magenta, yellow, and black inks in arbitrary colour pictures

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Abstract

This paper is concerned with the offset lithographic colour printing. To obtain high quality colour prints, given proportions of cyan (C), magenta (M), yellow (Y), and black (K) inks (four primary inks used in the printing process) should be accurately maintained in any area of the printed picture. To accomplish the task, the press operator needs to measure the printed result for assessing the proportions and use the measurement results to reduce the colour deviations. Specially designed colour bars are usually printed to enable the measurements. This paper presents an approach to estimate the proportions directly in colour pictures without using any dedicated areas. The proportions—the average amount of C, M, Y, and K inks in the area of interest—are estimated from the CCD colour camera RGB (L*a*b*) values recorded from that area. The local kernel ridge regression and the support vector regression are combined for obtaining the desired mapping L*a*b* ⇒ CMYK, which can be multi-valued.

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References

  1. Papas TN (1997) Model-based halftoning of color images. IEEE Trans Image Process 6:1014–1024

    Article  Google Scholar 

  2. Baqai FA, Allebach JP (2002) Computer-aided design of clustered-dot color screens based on a human visual system model. Proc IEEE 90:104–122

    Article  Google Scholar 

  3. Almutawa S, Moon Y (1993) Process drift control in lithographic printing: issues and connectionist expert system approach. Comput Ind 21:295–306

    Article  Google Scholar 

  4. Verikas A, Malmqvist K, Malmqvist L, Bergman L (1999) A new method for colour measurements in graphic arts. Color Res Appl 24:185–196

    Article  Google Scholar 

  5. Verikas A, Malmqvist K, Bergman L (2000) Neural networks based colour measuring for process monitoring and control in multicoloured newspaper printing. Neural Comput Appl 9:227–242

    Article  MATH  Google Scholar 

  6. Verikas A, Bergman L, Malmqvist K, Bacauskiene M (2003) Neural modelling and control of the offset printing process. In: Proceedings of the IASTED International Conference “Neural Networks and Computational Intelligence”. IASTED, Cancun, pp 130–135

  7. Hong G, Luo MR, Rhodes PA (2001) A study of digital camera colorimetric characterization based on polynomial modeling. Color Res Appl 26:76–84

    Article  Google Scholar 

  8. Brydges D, Deppner F, Kunzli H, Heuberger K, Hersch RD (1998) Application of a 3-CCD color camera for colorimetric and densitometric measurements. In: SPIE Proceedings, vol. 3,300, pp 292–301

  9. Bergman L, Verikas A, Bacauskiene M (2005) Unsupervised colour image segmentation applied to printing quality assessment. Image Vis Comput 23:417–425

    Article  Google Scholar 

  10. Sodergard C, Launonen R, Aikas J (1996) Inspection of colour printing quality. Intern J Pattern Recognit Artif Intell 10:115–128

    Article  Google Scholar 

  11. Wyszecki G, Stiles WS (1982) Color science. Concepts and methods, quantitative data and formulae, 2nd edn. Wiley, New York

    Google Scholar 

  12. Tominaga S (1998) Color control of printers by neural networks. J Electron Imaging 7:664–671

    Article  Google Scholar 

  13. Xia M, Saber E, Sharma G, Tekalp AM (1999) End-to-end color printer calibration by total least squares regression. IEEE Trans Image Process 8:700–716

    Article  Google Scholar 

  14. Balasubramanian R (1999) Optimization of the spectral Neugebauer model for printer characterization. J Electron Imaging 8:156–166

    Article  Google Scholar 

  15. Artusi A, Wilkie A (2003) Novel color printer characterization model. J Electron Imaging 12:448–458

    Article  Google Scholar 

  16. Bishop CM (1995) Neural networks for pattern recognition. Clarendon Press, Oxford

    Google Scholar 

  17. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge

    Google Scholar 

  18. Verikas A, Lipnickas A, Malmqvist K, Bacauskiene M, Gelzinis A (1999) Soft combination of neural classifiers: a comparative study. Pattern Recognit Lett 20:429–444

    Article  Google Scholar 

  19. Rhodes W (1989) Fifty years of the Neugebauer equations. In: Proceedings SPIE, vol 1184, pp 7–18

  20. Bergman L, Verikas A (2004) Intelligent monitoring of the offset printing process. In: Proceedings of the 2nd IASTED international conference “Neural Networks and Computational Intelligence”. Grindelwald, Switzerland, pp 173–178

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Correspondence to Antanas Verikas.

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Verikas, A., Bacauskiene, M. & Nilsson, CM. Estimating the amount of cyan, magenta, yellow, and black inks in arbitrary colour pictures. Neural Comput & Applic 16, 187–195 (2007). https://doi.org/10.1007/s00521-006-0066-6

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  • DOI: https://doi.org/10.1007/s00521-006-0066-6

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