Title :
A neural network approach for RGB to YMCK color conversion
Author :
Abe, Satoshi ; Marcu, Gabriel
Author_Institution :
Dept. of Inf. Sci., Tokyo Univ., Japan
Abstract :
Printing applications require to convert RGB displayable pictures into four printing process components: yellow, magenta, cyan and black. In almost all cases, the RGB colors on the display differ from YMCK printed colors. The YMCK printed colors can be simulated on the RGB display, using specific models for color reproduction on display and printer. In the paper, a neural network method is proposed as an alternative solution for RGB-YMCK color conversion, in order to simulate on RGB display the YMCK printed colors. The Neugebauer model was used to provide the training data for network. The general RGB simulation process of the printed YMCK colors represents a not bidirectional color conversion, so that, the network finds on possible transformation with a certain probability, strongly dependent on the learning data which determines the weights of the neural network
Keywords :
colour graphics; learning (artificial intelligence); neural nets; printing; probability; Neugebauer model; RGB; RGB display; RGB-YMCK color conversion; YMCK; YMCK printed colors; bidirectional color conversion; color conversion; color reproduction; learning data; neural network approach; printer; printing applications; probability; training data; weights; Cathode ray tubes; Color; Displays; Image converters; Ink; Neural networks; Phosphors; Printers; Printing; Reflection;
Conference_Titel :
TENCON '94. IEEE Region 10's Ninth Annual International Conference. Theme: Frontiers of Computer Technology. Proceedings of 1994
Print_ISBN :
0-7803-1862-5
DOI :
10.1109/TENCON.1994.369345