Color: from craft to computation

COLOR:
FROM CRAFT
TO COMPUTATION
Dr. Ján Morovič
Senior Color Scientist, Hewlett–Packard Company
Director of CIE Division 8: Image Technology

German Color Group’s 18th Image Processing Workshop (27/09/2012)

© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

OUTLINE

• I learned this color from my granny.

• Science starts creeping in ...

• Whoa! How do I do that in Excel?

• Come on! Surely that’s not color?!

• Are we done?


HEREDITARY COLOR

• Oooooo, when I mix these I get a nice red!

• But, when I try, it comes out brown?! :(

• Master Leonardo taught me a lovely dragon’s blood!
(Err, it just comes from a gum tree ...)

• Poor Winifred was killed by orpiment †

Trial and error, bad repeatability, superstition, health hazard


BUT: potential for great results!


MAKE WAY FOR SCIENCE!

Did you know that:

• Color is dominant wavelength!

• Color is spectral power distribution!

• Color is cone response!

• Memory colors are a special case! Sequence of ﬁrmly stated, but changing claims ...

• There is no metamerism in trichromatic color reproduction!

BUT: WE DO KNOW MORE

• Deﬁcient color vision • Color appearance • Color preference
phenomena
• Cone sensitivities • Color diﬀerence
• Chromatic adaptation
• Retinal center–surround • Metamerism
organization • Color constancy
• Colorant mixing
• Opponent color signals • Color memory
• Device characterization
• Visual pathway • Color esthetics


AND WE CAN BUILD TOOLS
• Color appearance models: CIECAM02 → “What will this surface look like under this
light, against this background and seen from this distance?”

• Color difference equations: CIEDE2000 → “How different is this pair of colors as
compared to that other pair?”

• Contrast sensitivity functions: CSFs → “How distinctly will I see a given difference
when it occurs at a given spatial frequency?”

• That let us provide solutions for: Process control (color characterization,
calibration), color and image enhancement, color specification and communication,
colorant recipe formulation, color management, color quantization for
compression, color in machine vision, color in telemedicine, color in motion pictures

BUT, THESE TOOLS ARE SIMPLISTIC!
• Color appearance models don’t follow physiology of human visual
system → approximations with data–fitted parameters

• Color difference, uniform color spaces, color rendering indices, chromatic
adaptation functions use separate computational mechanisms → ought
to derive from single, unified model of color perception

• Most models and metrics could be worked out with a calculator and
easily with Excel

• AND: Computational capabilities have evolved
far beyond spreadsheets!


WHAT DO YOU MEAN
“BIG DATA”?
?


THE COMPUTATIONAL REVOLUTION

• iCAM

• LAB2000HL

• Metamer constrained color correction

• Spectral printing

• HANS


ICAM & LAB2000HL
Spatial processing
to account for
(local) adaptation/
sharpening

Color space
uniformity not
achievable using
single set of
formulæ.

Fairchild & Johnson Lissner & Urban
JEI, 2004
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. IEEE TIP, 2012

MCCC

Scanner/camera characterization with one–to–many
relationship between sensor RGB and all spectra that could
have resulted in it: greater accuracy and level of realism

P. Morovič & Finlayson
JOSA, 2006

SPECTRAL PRINTING

Urban Derhak
JD, 2009 ICC DPD, 2011


HANS: WHO WHAT IS THAT?
printer
(inks, media, WS, FW, pipeline)!

artist!
Printing
as mosaic assembly

Separation: from ink amount
selection to Neugebauer
Primary (NP) statistics

mosaic!

print!
Halftoning: from per ink
continuous levels to discrete
drops to single ink drop stack
(NP) from local statistics

J. Morovič, P. Morovič, et al. tesserae/ Neugebauer Primaries (NPs) /
IEEE TIP, 2011 tiles! at-pixel ink drop states!


HANS COLOR WORKFLOW
LAB + spot colors! RGB/CMYK!
‘Upstream’/
External workﬂow Source content (RGB, Contone
Conventional!
CMYK, Lab, spot colors, Color device color
remains (un)tagged)! management! space!
unchanged (ICC Color
separation!
color management Halftone Contone
to device color ink space! Halftoning! ink space!
data) ink channels!

LAB + spot colors! RGB/CMYK!
Source content (RGB, Color Contone device
CMYK, Lab, spot colors, management! color space! Separation and
(un)tagged)! halftoning ‘talk’ in
HANS!

Color more speciﬁc
separation!
Halftone Contone terms and can
ink space! Halftoning! NPac space! ‘say’ new things/
ink channels! Neugebauer Primary channels! patterns.


BUT WHY BOTHER?
Alternative basic Alternative basic
ink combinations:! Print color! NPacs:!
• Vastly increased space of
addressable halftone
2! 6086!
(in 4D)! (in 34=81D)!
patterns, each having
speciﬁc properties (grain, ink
use, …): knD v. nD
3000x
possible
patterns (same
• Starting point for
color)! W=70%! optimization selection of the
C1=2%! pattern per color that has
M1=5%! ‘best’ properties.
Y1=5%!
W=79%! K1=6%! • Access to increased color
Y1=2%! C1M1=1%! gamut (convex combinations)
W=77%! K1=14%! C1K1=2%!
C=34%! C=7%! included! C1=1%! Y1K1=2%! M1K1=3%!
M=27%! Y=1%! Y1=2%! C2=1%! Y1K1=2%!
Y=28%! K=27%! K1=20%! K2=2%! C2=4%!


CMY METAMERS (TRICHROMACY?!)

12% W 0% W
35% C 51% C
0% M 0% M
29% Y 0% Y
24% CM 4% CM
0% CY 3% CY
0% MY 42% MY
0% CMY 0% CMY


20% LESS INK + 10% MORE GAMUT

+10% !60 !60

!40 !40

!20 !20

0 0

a*
a*
20 20

40 40

60 60

!40 !20 0 20 40 60 80 !40 !20 0 20 40 60 80
b* b*

CMYKcm @ 2dpp = 729 NPs Light ink use: current vs HANS

WAIT!
YOU CAN DO OTHER STUFF WITH COLOR?
• Cost: HANS, K as function of CSF

• Ease of use: color personalization

• Productivity: profiling chart optimization

• Reliability: closed loop system, auto-calibration/profiling/configuration

• Encryption?!


SPATIAL BLACK GENERATION

Kisilev et al.
CIC, 2011

COLOR ME EASY
printers/media photos

Personalize Color Output for P Printer/Media

Video 1: Video 2:
Adjust each image Encode adjustment as
individually for print modified ICC profile

New Media/Printer? New Media/Printer?
Adjust all photos... Adjust profile(s) only.

P. Morovič et al.
CGIV, 2012

FASTER WITH LESS

∆E2000
proﬁling color samples

J. Morovič et al.
IEEE TIP, 2010

SAME, SAME & NOT DIFFERENT
Densitometer integrated in print carriage → print carriage
calibration without need of expert knowledge or
external devices (1997) Mirror
Electronics

GreatgMacbeth/Xrite spectrophotometer
integrated in printhead-shaped enclosure, part of Photodiode
print carriage → 2x color consistency versus Array
densitometer
Holographic
Built–in, ‘Green Button,’ single–click, high quality Grating
(gallery print) ICC proﬁling with robust defaults
→
Test Chart
consistent & accurate color •  ICC Profiling
•  Calibration
Shutter with
HP Z–series Designjet printers White Tile Lens
since 2006 White LED

IT’S ALL GARBLED UP!?
1. Photographer contacts PSP
Photograher to have his photos printed... Print Service Provider

3. PSP sends encryption
profile to photographer

5. Photographer sends
encrypted photos to PSP

4. Photographer converts
photos to encrypted space

6. PSP sends scrambled 2. Print service provider
image to correct printer to retrieves scrambling ICC
be printed (correct profile from printer of
unscramling profile is photographers choice
applied internally) (unscrambling internal to
printer)

device X provides public key

represent & decode &
encode interpret
device A LABX device X
using public using private
key ICC key ICC
device B LABY device Y
represent & decode &
encode interpret
device Y provides public key
P. Morovič et al.
CIC, 2010

THE FUTURE OF COLOR

“You can't connect the dots looking forward;
you can only connect them looking backwards.” –Steve Jobs
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
“The only way you can predict the future is to build it.” –Alan Kay


WHAT SHALL WE BUILD?

• Color as color: larger gamut, more resolution, better detail, better re–rendering, controlled
color on new materials and under new conditions; pro performance for consumers at
consumer prices; done properly in new applications (medicine, motion pictures, data
visualization & analysis)

• But: do we need it? Yes! Customers may not ask for it, but will appreciate and demand it once
they see it (à la Ford’s faster horses v. cars)

• Hidden color: cheaper HW/materials/resources, faster setup and performance, easier use
and application to new contexts (e.g., 3D printing), more predictable, more consistent

• Good color can also be like good typography – invisible.

ACKNOWLEDGEMENTS
• Peter Morovič • Albert Serra • Marc Rossinyol

• Jordi Arnabat • Rafa Gimenez • Santi Reyero

• Juan Manuel García–Reyero • Ramon Pastor • Ingeborg Tastl

• Marti Rius • Jep Tarradas • John Recker

• Jordi Vilar • Johan Lammens

• Angel Albarran • Yvan Richard • Philipp Urban

• Carlos Amselem • Patrick Chase

THANK YOU!

Color: from craft to computation

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Color: from craft to computation