color: from craft to computation

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

COLOR: FROM CRAFTTO COMPUTATION

Dr. Ján MorovičSenior Color Scientist, Hewlett–Packard CompanyDirector of CIE Division 8: Image Technology

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


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!

• There is no metamerism in trichromatic color reproduction!

Sequence of firmly stated, but changing claims ...


BUT: WE DO KNOW MORE

• Deficient color vision

• Cone sensitivities

• Retinal center–surround organization

• Opponent color signals

• Visual pathway

• Color appearance phenomena

• Chromatic adaptation

• Color constancy

• Color memory

• Color esthetics

• Color preference

• Color difference

• Metamerism

• Colorant mixing

• Device characterization


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

Lissner & UrbanIEEE TIP, 2012

Fairchild & JohnsonJEI, 2004

Spatial processing to account for

(local) adaptation/sharpening

Color space uniformity not

achievable using single set of

formulæ.


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 & FinlaysonJOSA, 2006


SPECTRAL PRINTING

DerhakICC DPD, 2011

UrbanJD, 2009


HANS: WHO WHAT IS THAT?

tesserae/ tiles!

Neugebauer Primaries (NPs) / at-pixel ink drop states!

artist!

mosaic! pr

int!

printer (inks, media, WS, FW, pipeline)!

Printing as mosaic assembly

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

Halftoning: from per ink continuous levels to discrete drops to single ink drop stack

(NP) from local statistics

J. Morovič, P. Morovič, et al.IEEE TIP, 2011


HANS COLOR WORKFLOW

ink channels!

RGB/CMYK!

Color separation!

LAB + spot colors!

Source content (RGB, CMYK, Lab, spot colors, (un)tagged)!

Contone device color space!

Contone ink space!

Halftone ink space!Co

nven

tion

al! Color

management!

Halftoning!

Neugebauer Primary channels!

LAB + spot colors!

ink channels!

RGB/CMYK!

Color separation!

Color management!

Halftoning!

Source content (RGB, CMYK, Lab, spot colors, (un)tagged)!

Contone device color space!

Contone NPac space!

Halftone ink space!

HA

NS!

‘Upstream’/External workflow

remains unchanged (ICC

color management to device color

data)

Separation and halftoning ‘talk’ in

more specific terms and can

‘say’ new things/patterns.


BUT WHY BOTHER?Alternative basic

NPacs:!6086!

(in 34=81D)!

Alternative basic ink combinations:!2!(in 4D)!

C=34%!M=27%!Y=28%!

C=7%!Y=1%!K=27%!

W=77%!C1=1%!Y1=2%!K1=20%!

W=79%!Y1=2%!K1=14%!Y1K1=2%!C2=1%!K2=2%!

W=70%!C1=2%!M1=5%!Y1=5%!K1=6%!C1M1=1%!C1K1=2%!M1K1=3%!Y1K1=2%!C2=4%!

Print color!

3000x possible

patterns (same color)!

included!

• Vastly increased space of addressable halftone patterns, each having specific properties (grain, ink use, …): knD v. nD

• Starting point for optimization selection of the pattern per color that has ‘best’ properties.

• Access to increased color gamut (convex combinations)


CMY METAMERS (TRICHROMACY?!)

12% W35% C 0% M29% Y24% CM 0% CY 0% MY 0% CMY

0% W51% C 0% M 0% Y 4% CM 3% CY42% MY 0% CMY


20% LESS INK + 10% MORE GAMUT

CMYKcm @ 2dpp = 729 NPs

!60

!40

!20

0

20

40

60

!40 !20 0 20 40 60 80

b*a*

!60

!40

!20

0

20

40

60

!40 !20 0 20 40 60 80

b*

a*

Light ink use: current vs HANS

+10%


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

P. Morovič et al.CGIV, 2012

photosprinters/media

Personalize Color Output for P Printer/Media

Video 2:Encode adjustment as modified ICC profile

Video 1:Adjust each image

individually for print

New Media/Printer?Adjust all photos...

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


FASTER WITH LESS

∆E20

00

profiling color samples

J. Morovič et al.IEEE TIP, 2010


SAME, SAME & NOT DIFFERENT

Test Chart •  ICC Profiling •  Calibration

White LED

Shutter with White Tile Lens

Holographic Grating

Photodiode Array

Mirror Electronics

print carriage Densitometer integrated in print carriage → calibration without need of expert knowledge or

external devices (1997)

GreatgMacbeth/Xrite spectrophotometer integrated in printhead-shaped enclosure, part of

print carriage → 2x color consistency versus densitometer

Built–in, ‘Green Button,’ single–click, high quality (gallery print) ICC profiling with robust defaults

→ consistent & accurate color

HP Z–series Designjet printerssince 2006


IT’S ALL GARBLED UP!?

device A LABX device X

represent &encode

decode &interpret

device B device YLABYrepresent &

encodedecode &interpret

using publickey ICC

using privatekey ICC

device X provides public key

device Y provides public key

Photograher Print Service Provider1. Photographer contacts PSP

to have his photos printed...

3. PSP sends encryptionprofile to photographer

5. Photographer sendsencrypted photos to PSP

2. Print service provider retrieves scrambling ICC

profile from printer of photographers choice

(unscrambling internal to printer)

6. PSP sends scrambled image to correct printer to

be printed (correct unscramling profile is

applied internally)

4. Photographer convertsphotos to encrypted space

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

• Jordi Arnabat

• Juan Manuel García–Reyero

• Marti Rius

• Jordi Vilar

• Angel Albarran

• Carlos Amselem

• Albert Serra

• Rafa Gimenez

• Ramon Pastor

• Jep Tarradas

• Johan Lammens

• Yvan Richard

• Patrick Chase

• Marc Rossinyol

• Santi Reyero

• Ingeborg Tastl

• John Recker

• Philipp Urban


THANK YOU!

color: from craft to computation

Technology

color specication

color management

color quantization

color dierence equations

uniform color spaces

color rendering indices

color appearance models

health hazard copyright