hans print smoothness optimization and continuous control

© Copyright 2017 HP Inc.

HANS Print Smoothness Optimization and Continuous Control

Ján Morovič & Peter MorovičHP Inc., Barcelona, Catalonia, Spain

Presented at CIC 25, 14 September 2017, Lillehammer, Norway


Outline

• Once upon a time in New Mexico

• HANS basics

• Optimizing for smoothness

• Results

• Summary


“Is the set of all possible spectra convex?”

Albuquerque, NM 10th November 2007

Once upon a time in New Mexico


From Maxwell to “print-well”


“What would happen if we controlled the Neugebauer Primary at each halftone pixel,

instead of only ink amounts?”


MuchMuch more choice

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

Full gamut choices

Ink space: 2.8 x 1014

HANS:1.5 x 10172

Better ink efficiency & color gamut (CIC18, NIP ’11, IEEE TIP ’11)

CMY metamers (CIC19)

Spectral printing (CIC20)

8-vertex pipeline (CIC21)

Color & spectral print preview (CIC23)

Large gamut and spectral display (CIC24, JOSA A ’16)

Single-matrix halftoning (CIC24, IEEE TIP ’17)

Print smoothness (CIC25)… one more thing → HANS X (CIC25)

Ink-vectors NPacsD # D #

CMY 1dpp 8bit 3 107 8 1014

CMYK 2dpp 8bit 4 109 81 1079

CMYK 2dpp 12bit 4 1014 81 10172

Atoms in universe 1078-1082


Optimizing for smoothness:An engineering approach

• How do I find the patterns that result in the smoothest prints? • Define smoothness metric

• Needs to be fast (applied to millions of patterns)

• Enough if it is monotonic with perceived smoothness ← pick smoothest from among set

• Generate loads of pattern statistics (NPacs) & group by color

• Evaluate metamer set members

• Select smoothest pattern for each color

• Build look-up table from smoothest patterns


16 color patches (NPacs + printed)

A simple print smoothness metric

lowgrain

mediumgrain

highgrain

6 observers

perceived smoothness

digital halftones (predicted color at pixel)

metrics (σ-based, w/ or w/o CSF, w/ or w/o YN)

predicted smoothness

reorder for prediction

originalsimplestbest


Smoothness optimization• Find smoothest patterns spanning the available

color gamut: • Generate NPacs using RANSAC, • Predict colorimetry & smoothness, • Subdivide color space into bins (paramer

sets), assign NPacs to bins, • For each bin, select NPac with highest

smoothness score • Printing system: CMYK on HP Heavyweight

Coated paper, w/ up to two drops per ink per halftone pixel → 3^4=81 NPs

• Color model: RONT model (CIC23) w/ mean of 0.9, 95th perc. of 2.2 and max. of 3.4 ∆E2000

• Bins: 25^3 • NPacs generated, evaluated and binned:

13 171 174 • Result: 1257 non-empty bins

NPacs with (top) lowest and (bottom) highest grain. NPac markers shows color and size indicates grain. Black lines

connect the NPacs to centroid of bin containing it.


Results

highest smoothness

lowest smoothness


One more thing: continuous control

LUT with highest smoothness (Higher cost)

LUT with lowest smoothness (Lower cost)

w 1-w


Summary

10th anniversary of HANS(born at CIC 15)

Pixel content control instead of colorant control opens up new possibilities

Simple, engineering approach to smoothness optimization delivers strong results

HANS domain enables continuous control 🎂


Acknowledgements

• Africa Real

• Albert Serra

• Annarosa Multari

• Hector Gomez

• Joan Enric García

• Joan Manel García Reyero

• Jordi Arnabat

• Martí Rius

• Pere Gasparin

• Victor Diego

• Xavi Fariña

hans print smoothness optimization and continuous control

Engineering