This document summarizes a keynote presentation about future opportunities in color science and imaging. It discusses moving from benchtop spectrophotometers to integrating color measurement in printers. It also explores printing flexible displays, digital textiles, and using machine learning to automatically name colors in images at a pixel level. The presentation raises questions about new types of colorants beyond dyes and pigments that could be enabled by nanotechnology.

1. Color Science & Imaging:
Future Opportunities
Nathan Moroney,
Principal Scientist,
HP Labs,
Print Production Automation Lab

2. Overview
• Acknowledge that this talk is one of the keynotes
from the MCSL25th Anniversary symposium
• Approach the topic as a question of color
extrapolation
• Format the presentation as a series of questions
• First portion are a broad sampling of opportunities
• Last part is more specific to my ongoing research
interests
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3. Bigger
Future of Color Imaging
Smaller
Novel
Personal
Intuitive
Authentic
Pervasive
Hybrid
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4. Q: What tool would you want to
have when you meet the president?
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5. (Digital) Cameras
Color photograph taken in Iraq on the cover of the Wall Street Journal © Zuma Press
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6. Q: What’s a good way to remember
something that never happened?
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7. (Digital) Printing
Ink jet print of me with ink jet printed Bill & Dave.
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8. Q: A Softball is to a ping-pong ball
as …?
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9. Ink Jet Drop Volumes
• Five years ago 30 to 50 picoliters
• Where a picoliter is 1/1,000,000,000,000 liter
• Currently as low as 2 to 4 picoliters,
− with an announcement of 1 picoliter drop (~20 microns)
− example application is printing prototype flexible displays
• Roughly from a softball to ping-pong ball
• Spot sizes from 150 microns to 50 microns
• See also image sensor detector sizes
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10. Q: How long does it take to get a
spectrophotometer from a benchtop
to a printer?
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11. 1929: Benchtop
Dr. Arthur Hardy with his spectrophotometer.
Journal American Oil Chemists Society
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12. 2009: Printers
So that’s about 80 years from benchtop to printer.
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13. Q: Is there more to colorants than
dyes, pigments and the occasional
lake?
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14. Nanoscale Structures
Graphene created through abrasion of graphite?
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15. Nanoscale Structural Color
• Carbon nanotubes with 10 nm diameters can have
reflectances of < 0.05%
− Z. Yang et al, “Experimental Observation of an Extremely
Dark Material Made by a low Density Nanotube Array”,
Nano Letters, (2008)
− P. Vukusic et al, “Structurally assisted blackness in
butterfly wings”, Proc. R. Soc. Lond. B, S237-239 Suppl.
4, (2004).
− X. Zhou et al, “Color Detection Using Chromophore-
Nanotube Hybrid Devices”, Nano Letters (2009).
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16. Q: Is Variable Data Printing just
about paper?
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17. Variable Data Textiles
• Spoonflower.com
− CIELAB encoded
textile designs
uploaded to a
web site and then
digitally printed
• Digital
personalization in
this case is color
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18. Q: When is printing not about
making prints?
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19. Flexible Displays
HP Labs – Information Surfaces Lab
Roll-to-roll self-aligned imprint lithography flexible displays.
Color properties a key consideration.
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20. Q: What’s a reference magazine?
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21. The Color Thesaurus
• Magcloud.com
• Web
service
for indigo
printing of
magazines
• Mashing of
physical
form
factors
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22. Q: How many colors per feet per
minute are there in a high speed
inkjet press?
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23. Inkjet Web Press: Speed
400 Feet per Minute
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24. Inkjet Web Press: and Color
1200 x 600 dpi addressability
Four Color CMYK Printing
From pages per minute
to miles per hour…
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25. Q: Seen any interesting color
imaging in a museum lately?
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26. New York MOMA: April 2009
The Printed Picture Exhibit:
“the enormous social impact of multiple images.”
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27. Chromaticity Diagrams @ MOMA!
“If today's complex
tools are not carefully
calibrated and
controlled they wreak
havoc.”
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28. Digital Presses @ MOMA
“In the digital realm
it is no longer
possible for a
photographer or
printer to develop an
intuitive grasp of
materials and
processes simply by
using them.”
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29. Q: What does a book with 5000
authors look like?
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30. Unconstrained Color Naming on the Web =
A Book with 5000 Authors
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31. Q: What do you mean, your
computer is better at naming colors
than you are?
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32. Machine Color Naming
• Every pixel gets a color name
− Color, the only meaningful cognitive category for a single
pixel
• Based on large scale corpus of color names
collected online
• Basis for lexical image processing
• My computer knows more color names and color
name boundaries than I do.
• In fact it taught me chartreuse.
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33. Q: So the color naming debate is not
just about the universalist versus
relativist philosophical argument?
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34. Prototype Theory vs. Fuzzy Logic
Original Process
CIELAB each pixel
Slices one at a
time
Learn
Prototype centroids,
Theory need a
distance
metric
Learn
Fuzzy memberships,
Logic need full
memberships
in a color
space
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35. Q: How does color naming pixels
help?
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36. Lexical Quantization
• Massive, non-uniform, image independent, natural
language partitioning of color space
24-Bit Original Uniform Device Lexical
Quantization Quantization
(27 colors) (25 names)
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37. Lexical Quantization: Image
24-Bit Original Uniform Device Lexical
Quantization Quantization
(27 colors) (25 names)
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38. Q: Is there an aesthetics of
abstraction?
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39. Non-Photorealistic Rendering
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40. Future Opportunities
• Pages per minute to miles per hour
• Digital is the original, and it’s museum-worthy
• Variable data color grows out
• Race for flexible color displays
• Innovations on the nano-scale
• Numbers to names, and back again
• Questions?
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