Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Spectral modeling of a six-color inkjet printer

After customizing an Epson Stylus Photo 1200 by adding a continuous-feed ink system and a cyan, magenta, yellow, black, orange and green ink set, a series of research tasks were carried out to build a full spectral model of the printers output. First, various forward printer models were tested using the fifteen two color combinations of the printer. Yule- Nielsen-spectral-Neugebauer (YNSN) was selected as the forward model and its accuracy tested throughout the colorant space. It was found to be highly accurate, performing as well as a more complex local, cellular version. Next, the performance of nonlinear optimization-routine algorithms were evaluated for their ability to efficiently invert the YNSN model. A quasi-Newton based algorithm designed by Davidon, Fletcher and Powell (DFP) was found to give the best performance when combined with starting values produced from the non-negative least squares fit of single-constant Kubelka- Munk. The accuracy of the inverse model was tested and different optimization objective functions were evaluated. A multistage objective function based on minimizing spectral RMS error and then colorimetric error was found to give highly accurate matches with low metameric potential. Finally, the relationship between the number of printing inks and the ability to eliminate metamerism was explored

N-colour separation methods for accurate reproduction of spot colours

In packaging, spot colours are used to print key information like brand logos and elements for which the colour accuracy is critical. The present study investigates methods to aid the accurate reproduction of these spot colours with the n-colour printing process. Typical n-colour printing systems consist of supplementary inks in addition to the usual CMYK inks. Adding these inks to the traditional CMYK set increases the attainable colour gamut, but the added complexity creates several challenges in generating suitable colour separations for rendering colour images. In this project, the n-colour separation is achieved by the use of additional sectors for intermediate inks. Each sector contains four inks with the achromatic ink (black) common to all sectors. This allows the extension of the principles of the CMYK printing process to these additional sectors. The methods developed in this study can be generalised to any number of inks. The project explores various aspects of the n-colour printing process including the forward characterisation methods, gamut prediction of the n-colour process and the inverse characterisation to calculate the n-colour separation for target spot colours. The scope of the study covers different printing technologies including lithographic offset, flexographic, thermal sublimation and inkjet printing. A new method is proposed to characterise the printing devices. This method, the spot colour overprint (SCOP) model, was evaluated for the n-colour printing process with different printing technologies. In addition, a set of real-world spot colours were converted to n-colour separations and printed with the 7-colour printing process to evaluate against the original spot colours. The results show that the proposed methods can be effectively used to replace the spot coloured inks with the n-colour printing process. This can save significant material, time and costs in the packaging industry

Spectral printing of paintings using a seven-color digital press

The human visual system is trichromatic and therefore reduces higher dimensional spectral data to three dimensions. Two stimuli with different spectral power curve shapes can result in the same cone response and therefore match each other. Color reproduction systems take advantage of this effect and match color by creating the same cone response as the original but with different colorants. ICC color management transforms all colors into a three-dimensional reference color space, which is independent from any input or output devices. This concept works well for a single defined observer and illumination conditions, but in practice, it is not possible to control viewing conditions leading to severe color mismatches, particularly for paintings. Paintings pose unique challenges because of the large variety of available colorants resulting in a very large color gamut and considerable spectral variability. This research explored spectral color reproduction using a seven-color electrophotographic printing process, the HP Indigo 7000. Because of the restriction to seven inks from the 12 basic inks supplied with the press, the research identified both the optimal seven inks and a set of eight artist paints which can be spectrally reproduced. The set of inks was Cyan, Magenta, Yellow, Black, Reflex Blue, Violet and Orange. The eight paints were Cadmium Red Medium, Cadmium Orange, Cadmium Yellow Light, Dioxazine Purple, Phthalo Blue Green Shade, Ultramarine Blue, Quinacridone Crimson and Carbon Black. The selection was based on both theoretical and experimental analyses. The final testing was computational indicating the possibility of both spectral and colorimetric color reproduction of paintings

Modeling and Halftoning for Multichannel Printers: A Spectral Approach

Printing has been has been the major communication medium for many centuries. In the last twenty years, multichannel printing has brought new opportunities and challenges. Beside of extended colour gamut of the multichannel printer, the opportunity was presented to use a multichannel printer for ‘spectral printing’. The aim of spectral printing is typically the same as for colour printing; that is, to match input signal with printing specific ink combinations. In order to control printers so that the combination or mixture of inks results in specific colour or spectra requires a spectral reflectance printer model that estimates reflectance spectra from nominal dot coverage. The printer models have one of the key roles in accurate communication of colour to the printed media. Accordingly, this has been one of the most active research areas in printing. The research direction was toward improvement of the model accuracy, model simplicity and toward minimal resources used by the model in terms of computational power and usage of material. The contribution of the work included in the thesis is also directed toward improvement of the printer models but for the multichannel printing. The thesis is focused primarily on improving existing spectral printer models and developing a new model. In addition, the aim was to develop and implement a multichannel halftoning method which should provide with high image quality. Therefore, the research goals of the thesis were: maximal accuracy of printer models, optimal resource usage and maximal image quality of halftoning and whole spectral reproduction system. Maximal colour accuracy of a model but with the least resources used is achieved by optimizing printer model calibration process. First, estimation of the physical and optical dot gain is performed with newly proposed method and model. Second, a custom training target is estimated using the proposed new method. These two proposed methods and one proposed model were at the same time the means of optimal resource usage, both in computational time and material. The third goal was satisfied with newly proposed halftoning method for multichannel printing. This method also satisfies the goal of optimal computational time but with maintaining high image quality. When applied in spectral reproduction workflow, this halftoning reduces noise induced in an inversion of the printer model. Finally, a case study was conducted on the practical use of multichannel printers and spectral reproduction workflow. In addition to a gamut comparison in colour space, it is shown that otherwise limited reach of spectral printing could potentially be used to simulate spectra and colour of textile fabrics

Deep learning for characterizing full-color 3D printers: accuracy, robustness, and data-efficiency

High-fidelity color and appearance reproduction via multi-material-jetting full-color 3D printing has seen increasing applications, including art and cultural artifacts preservation, product prototypes, game character figurines, stop-motion animated movie, and 3D-printed prostheses such as dental restorations or prosthetic eyes. To achieve high-quality appearance reproduction via full-color 3D printing, a prerequisite is an accurate optical printer model that is a predicting function from an arrangement or ratio of printing materials to the optical/visual properties (e.g. spectral reflectance, color, and translucency) of the resulting print. For appearance 3D printing, the model needs to be inverted to determine the printing material arrangement that reproduces distinct optical/visual properties such as color. Therefore, the accuracy of optical printer models plays a crucial role for the final print quality. The process of fitting an optical printer model's parameters for a printing system is called optical characterization, which requires test prints and optical measurements. The objective of developing a printer model is to maximize prediction performance such as accuracy, while minimizing optical characterization efforts including printing, post-processing, and measuring. In this thesis, I aim at leveraging deep learning to achieve holistically-performant optical printer models, in terms of three different performance aspects of optical printer models: 1) accuracy, 2) robustness, and 3) data efficiency. First, for model accuracy, we propose two deep learning-based printer models that both achieve high accuracies with only a moderate number of required training samples. Experiments show that both models outperform the traditional cellular Neugebauer model by large margins: up to 6 times higher accuracy, or, up to 10 times less data for a similar accuracy. The high accuracy could enhance or even enable color- and translucency-critical applications of 3D printing such as dental restorations or prosthetic eyes. Second, for model robustness, we propose a methodology to induce physically-plausible constraints and smoothness into deep learning-based optical printer models. Experiments show that the model not only almost always corrects implausible relationships between material arrangement and the resulting optical/visual properties, but also ensures significantly smoother predictions. The robustness and smoothness improvements are important to alleviate or avoid unacceptable banding artifacts on textures of the final printouts, particularly for applications where texture details must be preserved, such as for reproducing prosthetic eyes whose texture must match the companion (healthy) eye. Finally, for data efficiency, we propose a learning framework that significantly improves printer models' data efficiency by employing existing characterization data from other printers. We also propose a contrastive learning-based approach to learn dataset embeddings that are extra inputs required by the aforementioned learning framework. Experiments show that the learning framework can drastically reduce the number of required samples for achieving an application-specific prediction accuracy. For some printers, it requires only 10% of the samples to achieve a similar accuracy as the state-of-the-art model. The significant improvement in data efficiency makes it economically possible to frequently characterize 3D printers to achieve more consistent output across different printers over time, which is crucial for color- and translucency-critical individualized mass production. With these proposed deep learning-based methodologies significantly improving the three performance aspects (i.e. accuracy, robustness, and data efficiency), a holistically-performant optical printer model can be achieved, which is particularly important for color- and translucency-critical applications such as dental restorations or prosthetic eyes

Colorimetric characterization of flexographic process utilizing analytical models

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Improving the Yule-Nielsen modified spectral Neugebauer model by dot surface coverages depending on the ink superposition conditions

The improvement of Yule-Nielsen modified spectral Neugebauer model incorporating a single halftone reproduction curve of various ink by dot surfaces coverages depending on the ink superposition conditions was analyzed. In the first model, the ink spreading which occurs when an ink halftone is printed on top of one or two solid inks was considered. In the second model, the concept was generalized to ink halftones printed on top or below solid inks. Results show that for thermal transfer prints at 75 lpi, ink spreading in all superposition condition reduces the mean prediction error