Measuring Anisotropic Light Scatter within Graphic Arts Papers For Modeling Optical Dot Gain

Optical dot gain (ODG) plays an important role for predicting the color of printed halftones. The detailed knowledge of light scatter within the printing substrate might improve the accuracy of printer models and can reduce the number of required training colors to fit the model to the printing system. We propose an apparatus and method for measuring local anisotropic light scatter within graphic arts paper for predicting ODG. The setup is a modification of existing approaches for a more robust determination of the light’s point spread function (PSF). To verify our approach we develop a simplified color prediction model for printed halftones that is based only on the reflectances of the fulltone color and the paper and incorporates the PSF for modeling the ODG. Our experiments show that the accuracy of the model in terms of color differences to the measured colors was improved by considering ODG. Introduction The reflectance spectrum of a print reproduction is a result of various factors including the spectral reflectance properties of inks and papers, the scattering behavior of incident light within the paper as well as the considered printing process and halftone method. Printing system properties such as the printer gamut or the optical dot gain (ODG) directly depend on these factors. In order to correctly control a printing process we need a mathematical model of the printer that accurately predicts spectral reflectances of the printout given a particular set of control values. We can find a wide variety of models for predicting spectral reflectances of multi-ink prints in literature. Wyble and Berns [1] distinguish two general types of printer models: regression based models and first principle models. They state that most models used in practices are regression based models. These models simulate the behavior of the system as a whole and are not necessarily based on physical principles. In general, test patches are printed and the model parameters are fitted to the reflectances measured. If one of the influencing factors, such as ink, paper or the printing process is changed, new test patches have to be printed and the model parameters have to be fitted again. It is very difficult to calculate correction factors to transfer the printer model to a different setup. Furthermore, the number of test patches required for accurately fitting the model to a setup usually increases drastically with the number of inks. The frequently used cellular Yule-Nielsen spectral Neugebauer model (CYNSN) [6, 7, 8, 9] with x grid points requires xk test patches, where k is the number of inks. Modeling a four-ink system utilizing five grid points results in 625 test patches. For a seven-ink system the number of test patches increases to 78,125. The measurement effort as well Expenses for fitting a printer model number of number of area covered with test inks test patches patches (5mm x 5mm) 4 (CMYK) 625 0.0156 m2 7 (CMYKRGB) 78,125 1.95 m2 as the required resources in terms of consumables to print these test patches exceed any practical dimension (see table). In recent years printing with seven (CMYKRGB) and more inks became increasingly important and new printers such as the Canon imagePROGRAF IPF6100 or HP Z3200 with up to 12 inks were introduced to the market. The described drawbacks of regression based models limit their applicability for those systems. In contrast, first principle models simulate the physical processes of the printing system. Even if we consider a printing system with more than four inks we can assume that only a few test patches are required to fit a first principle model. In this case, the overall number of model parameters of the first principle model should be significantly smaller than the number of parameters of the regression based model ( j+k ≪m see figure 1). Additionally, some of the results might be transferable to other printing setups. If only the paper differs, all parameters that are paper-independent have not to be changed. Hence, it is plausible that the effort for fitting a model to a setup can be reduced drastically using a first principle model. To better understand the concept of first principle models we need to look closer at the printing process: A raster image processor (RIP) calculates a digital halftone pattern from the printer control values (figure 1). The printer creates a physical image of this pattern onto the paper (concept images in figure 1). Usually,