Image Processing Reference
In-Depth Information
7.7
Simulate a node color convergence near the gamut boundary using a pole-
placement algorithm. Show
D
E
ab
convergence errors with and without the best
actuator algorithm. Use the Neugebauer model to represent the printer.
7.8
Simulate a node color convergence using a pole placement algorithm and a 4 to
3 control-based inversion. Use the Neugebauer model to represent the printer.
Choose any suitable GCR as nominal CMYK values for the node color. Show
D
E
ab
convergence errors with and without the best actuator algorithm.
7.9
Extend Problem 7.8 for additional in-gamut nodes (say, seven gray targets with
L*of25
0) along the neutral axis. If the
nodes are not in-gamut change the L* values so that they are inside the printer
-
90, in steps of 10 with a*
¼
b*
¼
s
gamut. Change nominal CMYK values and simulate the converge performance.
Do you still see near zero convergence error? Restrict the K separation using
K-restricted function of Equation 7.90 and simulate converge performance
with 3-to-3 control loop. What do you see?
7.10
Run a 4 to 3 control loop for the nodes in Problem 7.9. Is the convergence error
near zero at steady state? Plot actual K separation values along the neutral axis
from 4 to 3 control loop and superimpose the values on those obtained from
Equation 7.90. What do you see? Explain.
'
7.11
Produce a ray-based control model for mapping an out-of-gamut spot color
described with,
x
c
¼
[L
c
¼
80, a
c
¼
50, b
c
¼
30], and the gamut centroid,
x
0
¼
0]. Repeat this for another ray drawn toward the
same gamut centroid for another suitable out-of-gamut node color.
[L
0
¼
50, a
0
¼
0, b
0
¼
7.12
Black point compensation algorithm is used to retain shadow details in images
since many images contain colors that are darker than the darkest color a printer
can make. Using the algorithm described in Section 7.6.3 or a nonlinear input L*
to output L* mapping technique, design a black point compensation method that
can retain shadow details. Simulate the effects of parameters on shadow details.
ling example shown in Section 7.8 to build a 17
3
destination
7.13
Repeat the pro
ICC pro
le using MATLAB. Obtain Gamut corner plots and round trip
accuracy (overall and to corner points). Simulate the performance of some test
images.
REFERENCES
1.
International Color Consortium Specification, ICC. 1:2004-10 (Profile version 4.2.0.0),
Image technology colour management
le format, and data structure.
2. P. Heuberger, B. Ninness, T. Oliveira e Silva, P. Van den Hof, and B. Wahlberg,
Modeling and identification with orthogonal basis functions, 36th IEEE CDC Precon-
ference Workshop# 7, San Diego, CA, Dec. 1997.
3. L. Ljung, System Identication
—
Theory for the User, 2nd edn., PTR Prentice Hall,
Upper Saddle River, NJ, 1999.
4. S.R. Schmidt and R.G. Launsby, Understanding Industrial Designed Experiments, AIR
Academy Press & Associates, Colorado Springs, CO, ISBN 1-880156-03-2, 2005.
—
Architecture, pro
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