Study On Scanner Colorrimereic Characterization Based On Standard ICC

Scanner is one of the most important devices for obtaining images in printing system. Gradation of tone and color information of a color picture original often disappear in the scanning process and original manuscript can't be re-created trustily in virtue of devices' colorimetric characteristic, observation conditions, colorimetric ration and other influencing factors. As a result, the follow-up process can't be preceded regularly. Therefore, it is very necessary to study scanner's colorimetric characterization in accordance with standard ICC.AGFA DUOScan T2500 is the principal object of study. Standard IT8.7/2 and designed color target were scanned and handled dates individually. ICC profiles of scanner were established individually in the method of polynomial regression algorithm and BP neural network theory. Parameters influencing model precision were analyzed. These parameters include target color space, polynomial regression items, items number, different conversion types, modeling specimen quantity, number of concealed gradation, training time, number of network gradation and so on. A soft-ware establishing a scanner ICC profiles was exploited voluntarily and the color management outcome of the exploited software was estimated.It was indicated: Model precision was enhanced by injecting chromatic signal linear into polynomial regression algorithm and the precision of 20th model M5 is the best. Model precision of output space CIE LAB can be enhanced by adding constant terms and model precision of output space CIE XYZ can be enhanced by adding intersects. The number of concealed gradation neuron between 25 to30 can obtain better precision and training precision of BP neural network can be enhanced more validly by using three gradations 3-30-3 one step secant BP training way. The scanner profiles based on mixed models integrate N-component LUT-based input profiles advantage and Three-component matrix-based input profiles advantage. The decision of date transformation and the precision of profiles were enhanced. The profiles based on mixed models and output space CIE LAB PCS whose color management outcome is the best and whose precision is better than Profile Maker.