Color printer characterization using radial basis function networks

A key problem in multimedia systems is the faithful reproduction of color. One of the main reasons why this is a complicated issue are the different color reproduction technologies used by the various devices; displays use easily modeled additive color mixing, while printers use a subtractive process, the characterization of which is much more complex than that of self–luminous displays. In order to resolve these problems several processing steps are necessary, one of which is accurate device characterization. Our study examines different learning algorithms for one particular neural network technique which already has been found to be useful in related contexts, namely radial basis function network models, and proposes a modified learning algorithm which improves the colorimetric characterization process of printers. In particular our results show that is possible to obtain good performance by using a learning algorithm that is trained on only small sets of color samples, and use it to generate a larger look–up table (LUT) through use of multiple polynomial regression or an interpolation algorithm. We deem our findings to be a good start point for further studies on learning algorithms used in conjunction with this problem.