Scarica gli atti - Gruppo del Colore

covered is computed as the difference between the sum of the area coverage of the
inks and their overprints, with the constraint to be positive:
56
15
∑
p=
a = 1 − a
(4)
0
1
p
To effectively tune the model, the training set must be customized to employ all the
parameters. At the same time we want to have as few color patches to print and
measure for printer characterization as possible. The training set consists therefore
of ramps of eleven patches, ranging from the absence of ink to full ink coverage of
cyan, magenta, yellow, red, green, blue, black, cyan with black, magenta with
black, yellow with black, red with black, green with black and blue with black, for
a total of 143 samples.
Tab. 2 – Dot gain parameters for computing effective ink concentration using Equation 4
Index Neugebauer
Dot gain parameters
(p) primary
1 K Cck, λ Mmk, λ Yyk, λ Kk, λ
2 Y Ccy, λ Mmy, λ Yy, λ Kky, λ
3 YK Ccyk, λ Mmyk, λ Yyk, λ Kky, λ
4 M Ccm, λ Mm, λ Yym, λ Kkm, λ
5 MK Ccmk, λ Mmk, λ Yymk, λ Kkm, λ
6 R Ccmy, λ Mmy, λ Yym, λ Kkmy, λ
7 RK Ccmyk, λ Mmyk, λ Yymk, λ Kkmy, λ
8 C Cc, λ Mmc, λ Yyc, λ Kkc, λ
9 CK Cck, λ Mmck, λ Yyck, λ Kkc, λ
10 G Ccy, λ Mcmy, λ Yyc, λ Kkcy, λ
11 GK Ccyk, λ Mcmyk, λ Yyck, λ Kkcy, λ
12 B Ccm, λ Mmc, λ Ycmy, λ Kkcm, λ
13 BK Ccmk, λ Mmck, λ Ycmyk, λ Kkcm, λ
14 CMY Ccmy, λ Mcmy, λ Ycmy, λ Kcmyk, λ
15 CMYK Ccmyk, λ Mcmyk, λ Ycmyk, λ Kcmyk, λ
3. The Genetic Algorithm
A genetic algorithm is used to estimate the printer model parameters described
above. The choice of the representation of candidate solutions (individuals) and the
fitness function used to evaluate individuals are crucial factors in the effectiveness
of this genetic approach.
The genetic material, called the genome, of each individual must consist of the
minimum amount of data required to represent a solution to the problem.
Here the genome is an array of real numbers. In the printer model we have
introduced: