Spectral characterization involves building a model that relates the device dependent representation to the reflectance func- tion of the printed color, usually represented with a high number of reflectance samples at different wavelengths. Look-up table-based approaches, conventionally employed for colorimetric device char- acterization cannot be easily scaled to multispectral representa- tions, but methods for the analytical description of devices are re- quired. The article describes an innovative analytical printer model based on the Yule-Nielsen Spectral Neugebauer equation and for- mulated with a large number of degrees of freedom in order to ac- count for dot-gain, ink interactions, and printer driver operations. To estimate our model's parameters we use genetic algorithms. No as- sumption is made concerning the sequence of inks during printing, and the printers are treated as RGB devices (the printer-driver op- erations are included in the model). We have tested our character- ization method, which requires only about 130 measurements to train the learning algorithm, on four different inkjet printers, using different kinds of paper and drivers. The test set used for model evaluation was composed of 777 samples, uniformly distributed over the RGB color space.
Spectral-based printer modeling and characterization
Zuffi S;
2005
Abstract
Spectral characterization involves building a model that relates the device dependent representation to the reflectance func- tion of the printed color, usually represented with a high number of reflectance samples at different wavelengths. Look-up table-based approaches, conventionally employed for colorimetric device char- acterization cannot be easily scaled to multispectral representa- tions, but methods for the analytical description of devices are re- quired. The article describes an innovative analytical printer model based on the Yule-Nielsen Spectral Neugebauer equation and for- mulated with a large number of degrees of freedom in order to ac- count for dot-gain, ink interactions, and printer driver operations. To estimate our model's parameters we use genetic algorithms. No as- sumption is made concerning the sequence of inks during printing, and the printers are treated as RGB devices (the printer-driver op- erations are included in the model). We have tested our character- ization method, which requires only about 130 measurements to train the learning algorithm, on four different inkjet printers, using different kinds of paper and drivers. The test set used for model evaluation was composed of 777 samples, uniformly distributed over the RGB color space.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.