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Color Prediction and Separation Models in Printing

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Color Prediction and Separation Models in Printing Linköping Studies in Science and Technology Dissertation No. 1540 Color Prediction and Separation Models in Printing -Minimizing the Colorimetric and Spectral Differences employing Multiple Characterization Curves Yuanyuan Qu Department of Science and Technology Linköping University, SE-601 74 Norrköping, Sweden Norrköping 2013 Color Prediction and Separation Models in Printing - Minimizing the Colorimetric and Spectral Differences employing Multiple Characterization Curves © Yuanyuan Qu 2013 Image Reproduction and Graphic Design Research at Media and Information Technology Campus Norrköping, Linköping University SE-601 74 Norrköping, Sweden ISBN 978-91-7519-524-7 ISSN 0345-7524 Printed in Sweden by LIU-Tryck, Linköping, Sweden, 2013 ABSTRACT Color prediction models describe the relationship between the colorant combination and the resulting color after printing, under a specific set of printing conditions. Color separation models describe the inverse process, i.e. giving the ink combination required to reproduce a specific target color, under certain printing conditions. The two models are essential for print device characterization and calibration, from which the profiles used in color management systems are built up. Dot gain refers to an important phenomenon in printing causing the printed ink dots appear bigger than their reference size in the original bitmap. It is necessary and crucial to characterize dot gain correctly in color prediction models. Dot gain characterization for color printing is commonly presented by characterization curves for the primary inks, showing the reference coverage and the corresponding amount of dot gain. Most color prediction and separation models use a single characterization curve for each primary ink. Few researchers applied different curves taking into account the effect of ink superposition, but still, only one curve for each ink in a specific situation. In this thesis, the dot gain behavior of each ink is characterized using multiple characterization curves based on CIEXYZ tri-stimulus, without employing the n-factor that is used within the popular Yule-Nielsen model. Additionally, it is noticed from the experiments that the dot gain of a certain amount of ink is varying irregularly when ink superposition occurs. For higher color prediction accuracy, an effective coverage map is created to characterize dot gain based on CIEXYZ tri-stimulus values. With this map, given any reference ink combination, the effective coverage values of the involved inks are calculated by cubic interpolation, and then used to predict the tri-stimulus values of the printed colors. Experiments confirm that the prediction accuracy is improved significantly by using this effective coverage map in our basic color prediction model. Further investigation on the modified model is carried out in the aspect of training samples selection. Experiments show that using suitable training samples according to the dot gain characteristic of each ink can reduce the number of training samples that are required for the effective coverage map, without losing prediction accuracy. To fulfill the requirement of a spectral match in printing, the color prediction model based on CIEXYZ values is further extended to predict the spectral iii reflectance of the prints. The extension is simple and results in satisfying spectral prediction, which is verified by experiments. Based on this color prediction model (forward model), a simple color separation model (inverse model), minimizing the colorimetric and spectral differences, is also presented. The presented color separation model shows favorable stability while giving accurate color reproduction. Ink saving is feasible during the color separation by setting tolerances in colorimetric GLIIHUHQFHV ǻE94). The simplicity and high accuracy of the proposed color prediction and separation models prove their potential to be applied in color management in practical printing systems. In order to investigate the possibility of applying these models to multi- channel printing, color prediction for CMYLcLm prints, i.e. with the additional colorants light cyan and light magenta, is carried out using the forward model. The color prediction is implemented by treating the combination of C and Lc (M and Lm) as a new ink coordinate to replace the ink coordinate C (M) in our three-channel color prediction model. The corresponding prediction results are acceptable. Suggestions are also given for future work to simplify and modify the approach based on our simple color prediction model for CMYLmLc prints. iv ACKNOWLEDGEMENTS I would like to dedicate this dissertation to the people around me who have directly or indirectly supported me during my Ph.D study and research. This dissertation would not have been possible without their help and encouragement. First and foremost, I would like to express my sincere gratitude to my supervisor, Sasan Gooran, for his altruistic guidance, continuous support and stimulating suggestions; and for his patient help in writing throughout these works. His humor and friendliness brought me tremendous comfort and relaxation. My gratitude also goes to Daniel Nyström, who openly offers his support and knowledge to guide me as my co-supervisor. His suggestions and detailed comments added a lot to this thesis work. I would further like to thank Björn Kruse and Li Yang, for their guidance during my study, and for their friendly and wonderful conversations about research work and life experience. My deep gratitude goes to my colleagues and friends: to Mahziar, and Sara, for their warm friendship, and I am impressed by their obliging personality; to Paula, for her persistent encouragement and sharing of her stories and emotions; to Dag and his family, not only for the fantastic trips they brought to me out in Sweden nature, but also for their warmhearted care; to Gun-Britt and her lovely family, for their warm help and hospitality. Great thanks go to Lei Chen, from whom I learned and gained a lot to improve myself in life and study; also to Qing, Lei, Jingchen and Juan, Fahimeh, Jiang, Zhuangwei, Xiaodong, for their joyous company and cheerful supports. There are so many smiling faces around me every day. My deepest appreciation goes to them for creating such a friendly environment in ITN. I would also like to show my appreciation to the division for Media and Information Technology in Linköping University, as well as the the CSC (China Scholarship Council) for the financial supports. In my daily life I have been blessed with a cheering group of fellow, in Sweden and China, who are gratefully acknowledged with my love. My special thanks go to Hui, Ning and Lili, who teach me like sisters with their experiences. Last but not least, my heartfelt thanks go to my parents, my brother and his wife, who support me with their endless love and encouragement. Norrköping, August 2013 Yuanyuan Qu v vi PUBLICATIONS Qu, Y., Nyström, D. and Kruse, B., 2011. A new approach to estimate the 3-d surface of paper. Proc. TAGA (Technical Association of the Graphic Arts), Pittsburgh, PA. USA, pp. 321-339. (Also was presented in SSBA 2011, Sweden). Qu, Y. and Gooran, S., 2011. A simple color prediction model based on multiple dot gain curves. Proc. SPIE 7866, Color Imaging XVI: Displaying, Processing, Hardcopy, and Applications, San Francisco, California. USA, 7866, pp.786615-786615-8. Qu, Y. and Gooran, S., 2012. Simple color prediction model based on CIEXYZ using an effective coverage map. J. Imaging Sci. Technol., 56(1), pp. 0105061-105069. Qu, Y. and Gooran, S., 2012. Investigating the possibility of using fewer training samples -- in the color prediction model based on CIEXYZ using an effective coverage map. Proc. CGIV 2012(6th European Conference on Colour in Graphics, Imaging, and Vision), pp. 163-168. Qu, Y. and Gooran, S., 2013. Simple spectral color prediction model using multiple characterization curves. Proc. TAGA (Technical Association of the Graphic Arts), Portland, Oregon, USA. Qu, Y. and Gooran, S., 2013. A simple color separation model based on colorimetric and spectral data. Accepted to be published in the J. Print and Media Tech. Research. Qu, Y., Zitinski Elias, P. and Gooran, S., 2014. Color prediction modeling for five-channel CMYLcLm printing, Proc. SPIE, Color Imaging XIX: Displaying, Processing, Hardcopy, and Applications, San Francisco, California, USA, submitted. vii viii CONTENTS 1. INTRODUCTION....................................................................................... 1 1.1 Introduction......................................................................................................... 3 1.2 Background......................................................................................................... 3 1.3 Overview............................................................................................................. 5 2. COLOR FUNDAMENTALS...................................................................... 7 2.1 Color Vision........................................................................................................ 9 2.2 Color Attributes ................................................................................................ 10 2.3 Additive and Subtractive Color Mixing ............................................................ 12 2.4 Color Matching Functions................................................................................. 13 2.5 Colorimetry and CIE Color Spaces..................................................................
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