Abstract
In the digital printing process, reliable colour reproduction is commonly achieved by printer characterisation, which defines the correspondence between the input device control values and the output colour information. The cellular Yule–Nielsen spectral Neugebauer model, together with its variants, is widely adopted in this topic because of its superb colorimetric and spectral accuracy. However, it seems that current studies have neglected an inconspicuous defect in such models when characterising printers equipped with black ink. That is, the cellular structure of these models overemphasises the sampling for dark-tone colours, and thus leads to relatively large errors in light tones. In this paper, taking a CMYK printer as an example, a simple and effective solution is proposed with no need of extra sampling. With the aid of a newly built cellular spectral Neugebauer model for the embedded CMY printer, this approach optimises the printer characterisation for light tones, slightly improves the precision for middle tones while it maintains the accuracy for dark tones. The performance of the proposed method was evaluated with regard to three different kinds of substrates and the experimental results validated its improvement in spectral printer characterisation.
Highlights
Printer characterisation aims to reveal the relationship between the input and the output of the halftone printing process, and generates the optimal ink combination for a target colour [1,2,3,4,5,6].The first half of printer characterisation relates to a forward modelling in which a colour prediction model [7,8,9,10] is set up to predict the printed colours from the device control values
The ink limitation process Tink-limitation could be depicted as Equation (1), in which Ωdevice and Ωink-limitation respectively, denote the original device-value space and the printable region for a printer equipped with n inks, Φ represents the device control values after ink limitation, Φtotal−max denotes the maximum total ink amount, a(i) is the dot area of the ith Neugebauer primary defined by the Demichel equation [35], g(i) defines the control values of the Neugebauer primaries
As it is a measure relating to the light scattering effect in halftone prints, it is easy to infer that the optimal n value should not be consistent among different ink mixtures. From this point of view, a backward propagation artificial neural network (BPANN) approach was proposed in our previous work, with the aim of dynamically predicting the optimal n values for different ink combinations and our experiments showed that the cellular Yule–Nielsen modified spectral Neugebauer (CYNSN) model modified by this BPANN approach achieved a sound balance between accuracy and efficiency [19]
Summary
Printer characterisation aims to reveal the relationship between the input and the output of the halftone printing process, and generates the optimal ink combination for a target colour [1,2,3,4,5,6]. The second half of printer characterisation, which is termed as a backward process, aims at to invert the forward model by certain optimization algorithms and decomposes the target colour into control values of individual inks such as cyan, magenta, yellow and black (CMYK). Many mathematical models have been adopted for forward modelling, which includes a least-squares based polynomial function [22], the Kubelka–Munk model [23], the artificial neural network (ANN) [24], the spectral Neugebauer model [25], etc The high accuracy of the CYNSN-based spectral printer characterisation could be achieved by increasing the sampling nodes in the cellular structure. 7-grid-points CYNSN models, Wang et al set up a multi-ink printer characterisation workflow and achieved excellent forward and backward modelling accuracy [27,28].
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