Characterization Of Colorscanners Based On Artificial Neural Network

We research the principle of color scanner characterization and Support Vector Regression (SVR) respectively and then analyze the possibility of nonlinear transformation from scanner RGB color space to CIELAB color space based on Support Vector Regression (SVR). We built a model of scanner RGB color space to CIELAB color space based on SVR.To prove the accuracy of the model, choose IT8.7/2 color card as color target and make data simulation with MATLABR2009a software and figure out the color differences using CIELAB color difference formula and CIEDE2000 color difference formula. In the Experiment ,we use the scanner and chromaticity instrument to measure the RGB values and CIE values of all color lumps and divide the 288 color lumps into two groups (training and testing group) .The experimental results show that the similarity is more than 99% between predicted values and true values of L?、a?、b? . Based on CIELAB color difference formula, the average、the maximum and the minimum color differences of test set are 2.3143、5.7917 and 0.5073. Based on CIEDE2000 color difference formula, the average、the maximum and the minimum color differences of test set are 2.6597、5.2194 and 0.2065. From the results, we can make a conclusion that SVR can realize the nonlinear transformation from scanner RGB color space to CIELAB color space and the model satisfies the accuracy of scanner characterization. Therefore, SVR is a very promising tool for using in color scanner characterization management.