Modified Spectral Neugebauer Model for Printer Characterization

Abstract

Spectral Neugebauer model is widely used for spectral reflectance prediction during printer characterization. However, several factors reduce the predication precision. Thus, an improved cellular Yule-Nielson spectral Neugebauer (CYNSN) model is proposed, which modifies the traditional spectral Neugebauer model in three main aspects: (1) First, in order to adjust the nonlinearities between the predicated and measured spectral reflectance, an iteratively calculated Yule-Nielson exponent is added to the reflectance values within the Neugebauer equations.(2) Second, the quantity of Neugebauer primaries is increased by dividing the CMY colorant space into 4 3 uniform cellular subdomains. (3) Third, the mapping functions are developed to map the area coverages' theoretical values to their effective values within the subdomains, and the mapped values highly improve the matching degree of the predicated and measured reflectance values. In the experiment, four related spectral Neugebauer models are employed during printer characterization, which are the traditional spectral Neugebauer model, Yule-Nielson spectral Neugebauer (YNSN) model, traditional CYNSN model, and the modified CYNSN model, respectively. And the experimental results show the modified CYNSN model yields a significant improvement over the other spectral Neugebauer models, by comparing the characterization errors in the form of colorimetry and spectroscopy.

Optimizing the Spectral Characterisation of a CMYK Printer with Embedded CMY Printer Modelling

“…In a recent work, Sun et al revisited the optimization of the CYNSN model [20]. However, the accuracy of their CYNSN model was obviously lower than that of other relevant studies [19,26], which we believe is possibly due to insufficient consideration being given to the Yule-Nielsen n value.…”

“…However, as such a model is based on several theoretical assumptions (e.g., it is assumed that the ink spreading and light scattering among different ink mixtures is always consistent), which, in fact, are not perfectly true, it still suffers from several limitations when spectrally characterising a printer. Therefore, to further optimise the performance of the CYNSN model, much effort has been made [19,20,26].…”

“…Therefore, in several studies such a model is also named as a colour separation model [11][12][13][14]. In addition, since the technical progress in digital imaging has activated the concept of spectral colour reproduction for the reduction of metamerism [15][16][17][18], the studies of printer characterisation have also been extended to the spectral domain [19][20][21].…”

“…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. Among these models, the cellular Yule-Nielsen spectral Neugebauer (CYNSN) model, together with its variants, is perhaps the most preeminent model due to its intuition and precision [19,20,[26][27][28]. Such models calculate the printed reflectance spectrum with a weighted sum of participating Neugebauer-primary spectra and take several influential effects into consideration, such as mechanical and optical dot gain [7].…”

Design of four-band multispectral imaging system with one single-sensor

“…In addition, as the final output of the multispectral imaging system is the spectrum data of each pixel, the reflectance root mean square (RRMS) error is employed to evaluate the imaging system [33], which is the difference of the original and reconstructed reflectance data expressed as below.…”