Investigating the Possibility of Using Fewer Training Samples - In the Color Prediction Model based on CIEXYZ using an Effective Coverage Map

The goal of the present work is to reduce the number of the training samples used in our color prediction model based on CIEXYZ using an Effective Coverage Map while keeping satisfying prediction. A general approach is proposed in this paper to choose the best reference combination for the training samples. The approach is based on the dot gain behavior of each primary ink, which is characterized by three curves using CIEXYZ tri-stimulus values. The proposed approach is built in our model to predict the color values for the color prints using two different devices, i.e. a laser printer and an inkjet printer. For the laser printer the number of the training samples is reduced from 125 to 64 while still giving quite good result. The approach also shows that for the test laser printer it is possible to further cut this number to 53 with a satisfying result. For the inkjet printer the number of training samples for our model is reduced from 125 to 79 or 64, both giving satisfying results. Introduction The color prediction model investigated in this paper is using an effective coverage map based on CIEXYZ values to predict the values of the resulting color in halftone print. The most famous and almost the simplest prediction model of halftone prints is the Murray–Davies (MD) model in Equation 1, which describes the relationship between the reflectance spectrum of a color patch and the reflectance spectra of the involved full-tone ink and the white paper. ) ( ) 1 ( ) ( ) ( λ λ λ p i R a aR R − + = (1) where R(λ) is the predicted spectral reflectance and a is the fractional area of the ink. Ri(λ) is the spectral reflectance of the printed ink at full tone and Rp(λ) is the spectral reflectance of the substrate, i.e. paper. Because of the linear relationship between CIEXYZ values and the spectral reflectance, Equation 1 can be extended to Equation 2 based on CIEXYZ values, which is called Neugebauer’s equations.