Accurately previewing the appearance of a print job can make the difference between producing saleable output and wast- ing expensive materials and is a challenge to which a host of solu- tions already exist. However, what the majority of these have in common is that they base their predictions on the inputs to a print- ing system (e.g., continuous-tone data in ink channels) instead of its outputs (i.e., the halftone data that is then printed) and that they are only valid for a given set of choices already made in the print- ing system (e.g., color separation and halftoning). Alternatively, attempting to make appearance predictions using general-purpose models such as Kubelka Munk, Yule Nielsen and Neugebauer re- sults in limited performance on systems whose behavior diverges from these models’ assumptions, such as inkjet printing. As a result of such constraints, the resulting previews either work only under limited conditions or fail to predict some artifacts while erroneous- ly predicting others that do not materialize in print. The approach presented here takes advantage of the flexibility of the HANS framework and the insights into spectral correlation to deliver a print preview solution that can be applied to any printing system, that allows for the variation of fundamental imaging choices with- out the need for re-computing model parameters and that delivers ICC-profile-level accuracy.

1. Spectral and color prediction for arbitrary halftone
patterns: a drop-by-drop, WYSIWYG,
“ink on display” print preview
Peter Morovič, Ján Morovič, Xavier Fariña,
Pere Gasparin, Michel Encrenaz, Jordi Arnabat
Presented at 23rd IS&T Color and Imaging Conference, 21st October 2015, Darmstadt, Germany

2. Overview
• Motivation – why do we keep working on print modeling?
• Background – existing approaches & limitations
• An end-to-end print simulation approach
• Test setup
• Results & discussion
• Conclusions

3. Motivation
• Why?
• Digital print production:
many short-run jobs
• Bottleneck:
operator intervention
• Web-to-print workflows
increase cost of manual
overrides + greater need
for accurate print preview
before placing order
• To avoid re-printing
• To provide customer assurance
• Challenge: Make print preview close to final printed output as possible
• If not, decisions made on basis of preview are unreliable
& inaccuracies further aggravate getting to right print

4. Lacking components
• First, accurate model of
print color formation,
applicable to technologies
like inkjet
• Ink-media interactions are
complex, highly non-linear &
not well represented by
models developed for
analog printing
• Second, framework for
applying scaling to print
preview that reflects nature
of print color formation,
instead of assuming display
color formation.

5. Background: print spectral & color models
• Prediction of ink overprinting: Kubelka Munk
(1931)
• Per-ink K (absorption) and S (scattering),
derived from opacity and reflectance:
• where R∞ is reflectance of infinitely thick sample;
prediction made at wavelength λ
• Combining multiple ink layers is then
additive in K and S:
• where B is substrate, l is number of ink layers,
ci concentration and Ki absorption coefficient of
the i-th layer
Wang & Wang, 2007

6. Background: print spectral & color models
• Prediction of integrated halftone
color: Yule-Nielsen modified
Neugebauer
• Reflectance of entire halftone pattern
predicted from constituent ink dot
overlap colors:
• where R(λ) is reflectance of optically-
integrated halftone pattern
neighborhood, wj is relative area
coverage of the j-th Neugebauer
Primary – P, and n is Yule-Nielsen non-
linearity accounting for optical dot gain.
Side view
Colorants
Substrate
Appearance
W
C M Y
Neugebauer primaries
Materials
Example halftone
C M Y CMY
+ + =
NP areas
W=1/9
C=1/9
M=2/9
CM=2/9
CY=1/9
MY=1/9
CMY=1/9Colorant vector
[C,M,Y]=[5/9, 6/9,3/9]

7. Background: print spectral & color models
• Model summary:
• specific ways for predicting specific phenomena
• scattering, absorption, optical dot gain and optical
integration
• specific assumptions about properties of constituent
parts
• e.g., homogeneity of ink layers, interfaces, independence
of layer properties, infinite substrate thickness, etc.
• knowledge of certain component properties is a
prerequisite
• Reflectance of infinitely thick ink layers, scattering of
substrate, concentrations of inks, optical dot gain,
Neugebauer Primary area coverages.
• Practical challenges:
• difficult to know properties
• fundamental properties often estimated from
measurements
• Opportunity: think of models
merely as computational mechanisms

8. Background: ICC-based soft-proofing
• What: print color simulations (soft-proofs) obtained by building ICC profiles of
display and printing system and color managing print colorimetry to display
device color space
• treats printing system like a black box
• at best, result is color accurate at LUT nodes
• in-between, assumptions of linearity are made
• process is blind both to spatial features (e.g., grain) and to actual nature of
transitions between profile LUT nodes
• incl., mapping from device color space to printing system’s colorant space (i.e., color
separation) and consequences of specific halftoning used (e.g., when dots start overlapping).
• Limitations:
• no sense of print’s spatial features,
• makes transitions have a characteristically display-like look
• mismatch in artifacts (missing some from the print; introducing others not in print)

9. Background: ICC-based soft-proofing
Original
Soft-proof
Print photo
False
artifacts
Grain
Transitions

10. An end-to-end print simulation approach
3 key elements:
• Predicting print appearance from halftone data instead
of continuous-tone data input to a printing system after
color management.
• Extending printer modeling by a mechanism that
enforces spectral correlation.
• Printer model based scaling, instead of device color
space linear interpolation.

11. Element 1: Print-ready halftone data
• Pass print job through same
• color management,
• color separation,
• linearization and
• halftoning
• as used for printing.
• End result: halftone that could be
directly printed, but that will be used for
on-screen simulation instead.

12. Element 2: RONT model
• Starting point: Kubelka Munk + Yule-Nielsen
modified Neugebauer, with key parameters
being Reflectance, Opacity and the optical
dot-gain N exponent. (RON)
• BUT: RON does not represent inkjet systems
well
• Approach: add computational
mechanisms to model
• All of KM+YNN performed wavelength-by-
wavelength
• BUT: strong correlation between adjacent
spectral bands (e.g., Singh et al., 2003; Morovic
et al., 2014)

13. Element 2: RONT model
• Concept: adjust reflectance predictions in a
given band based on predictions made for its
neighbors.
• takes advantage of data from spectral
neighborhood
• allows for enforcing correlation found in natural
spectra that a per-band model may break
• Mechanism: matrix transformation of the
reflectances predicted using RON parameters
only
• To compute such matrix T, L2 norm between
intermediate RON reflectance predictions (RI) and
measured reflectances (RM) needs to be minimized:
that not all wavelengths have an equal spr
axis. A wavelength-by-wavelength view (F
ences between individual correlations in mo

14. Element 2: RONT model
• where R matrices have m rows (for m samples) and s columns (for s spectral
bands) and where f() is function specifying t terms to use in error minimization,
resulting in dimensions of f(RI) being m×t and those of T being t×s.
• f() may add constant term, cross terms and power terms
• in simple, hypothetical case of s=2, result of f() could be the following for a second
order case:
• Having computed T, its result is applied to predictions based on RON parameters:

15. Element 3: Print optical integration-based scaling
• simulate optical integration that takes place when print is viewed at different distances
• each display pixel represents optical integration of print halftone pixel neighborhood
• display pixel color needs to be computed using full RONT model for neighborhood, instead of as
mean of per-halftone-pixel predictions.

16. Test setup
Label Z6200-4 PWT-4 L310-4 L310-6
Printer (HP)
Desigjet
Z6200
PageWide
Technology
Latex 310 Latex 310
Substrate
HP
Heavyweight
Coated
HP
Heavyweight
Coated
Self-Adhesive
Vinyl
HP
Photorealistic
Paper
Inks
CMYK
aqueous
CMYK
aqueous
CMYK
latex
CMYKcm
latex
Max. drops per ink
per pixel 2 2 3 2
Neugebauer
Primaries 34=81 34=81 44=256 36=729
Training samples 756 620 3257 4808
Test
samples 459 620 3257 4808

17. Results (∆E2000)
System Model Median
95th
%tile
Max.
Z6200-4
RON 8.5 23.2 35.4
RONT 1.5 4.8 8.2
PWT-4
RON 3.2 8.1 15.6
RONT 0.9 2.2 3.4
L310-4
RON 4.1 11.2 29.9
RONT 1.3 2.8 6.5
L310-6
RON 1.7 4.9 10.6
RONT 0.9 2.2 7.7

18. Simulation
Print photo

19. Conclusions
• Successful print simulation can make the difference between
• wasting resources and frustrating (or even losing) customers,
• delivering salable products efficiently & with good customer experience.
• A new solution was presented here that:
• bases simulations on same data that drives printing systems – i.e., colorant channel
halftone data
• ensures that simulated halftone data is scaled for display in a way that respects its
local properties,
• Resulting simulations that are both more color-accurate
and represent print-specific features and limitations faithfully.
• Next steps: test new framework on broader variety of printer-substrate-ink
configurations and extended as necessary.