1. AM-FM Screen Design Using Donut Filters
Niranjan Damera-Venkata and Qian Lin
Hewlett-Packard Laboratories
1501 Page Mill Road, MS 1203
Palo Alto CA 94304

2. SPIE Electronic Imaging Conference - 01/22/04
4/25/2017
Outline
Halftoning methods
why use green-noise (AM-FM) halftoning?
Statistical properties of green-noise patterns
spatial statistics
spectral statistics
Design of optimal green-noise screens
optimized “donut” filters
Green-noise error diffusion
analysis of Levien error diffusion
noise shaping: role of the “donut” filter
new green noise error diffusion methods
Status
SPIE Electronic Imaging Conference - 01/22/04
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3. Conventional AM halftoning
4/25/2017
Conventional AM halftoning
Indigo’s “Sequin” halftoning
Dot frequency is fixed
Dot size varies to represent tone
Disadvantages
Rosette patterns
Tone jumps
Detail rendition suffers
Scan quality suffers
SPIE Electronic Imaging Conference - 01/22/04
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4. FM halftoning (a.k.a. “blue-noise”)
4/25/2017
FM halftoning (a.k.a. “blue-noise”)
Photo printers
Dot size is fixed
Dot frequency varies
Advantages
Does not cause Moiré
Rosette patterns eliminated
Tone jumps not abrupt
Better detail rendition
Better quality at consumer grade scan resolutions
Disadvantages
Depends heavily on fidelity of isolated dot reproduction
dot dropout and
banding in
highlights
clumping in the
midtones results
In grain
SPIE Electronic Imaging Conference - 01/22/04
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5. AM-FM halftoning (a.k.a. “green-noise”)
4/25/2017
AM-FM halftoning (a.k.a. “green-noise”)
AM-FM
Dot size and dot frequency varies
Advantages
Promise of the “best of both worlds”
Disadvantages
Design depends on particular dot formation characteristics
Difficult design problem
highlights
midtones
SPIE Electronic Imaging Conference - 01/22/04
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6. Green-noise statistics [Lau et al.]
4/25/2017
Green-noise statistics [Lau et al.]
G=0.10
G=0.25
Spatial
Spectral
SPIE Electronic Imaging Conference - 01/22/04
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7. SPIE Electronic Imaging Conference - 01/22/04
4/25/2017
AM-FM Screen design
Filter based methods
Donut Filters [Lin]
Optimal void and cluster approach
Donut filter parameters must be empirically chosen for each graylevel
ColorSmooth Dither [Lin and Allebach]
Computationally very expensive
Handles joint design for a set of colorants
Several parameters must be set empirically
Optimal method
Green Noise Mask [Lau, Arce and Gallagher]
Approximates green-noise Markov statistics in Maximum Likelihood sense
SPIE Electronic Imaging Conference - 01/22/04
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8. SPIE Electronic Imaging Conference - 01/22/04
4/25/2017
Donut filters
Dots added one at a time
Start off with well distributed dot centers and then grow them
Filter existing pattern with filter
Choose minimum location and add a dot there
Donut filter’s characteristic promotes dot clustering
Spectral
Spatial
SPIE Electronic Imaging Conference - 01/22/04
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9. SPIE Electronic Imaging Conference - 01/22/04
4/25/2017
Donut filters
Dots added one at a time
Start off with well distributed dot centers and then grow them
Filter existing pattern with filter
Choose minimum location and add a dot there
Donut filter’s characteristic promotes dot clustering
SPIE Electronic Imaging Conference - 01/22/04
HP_presentation_template

10. SPIE Electronic Imaging Conference - 01/22/04
4/25/2017
Optimal donut filters
Maximum Likelihood solution for dot placement problem is equivalent to finding min/max of filter output
SPIE Electronic Imaging Conference - 01/22/04
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11. Deriving the optimal donut filter
4/25/2017
Deriving the optimal donut filter
SPIE Electronic Imaging Conference - 01/22/04
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12. SPIE Electronic Imaging Conference - 01/22/04
4/25/2017
Results
G=22/255
G=42/255
G=62/255
G=82/255
SPIE Electronic Imaging Conference - 01/22/04
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13. SPIE Electronic Imaging Conference - 01/22/04
4/25/2017
Results
SPIE Electronic Imaging Conference - 01/22/04
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14. SPIE Electronic Imaging Conference - 01/22/04
4/25/2017
Results
SPIE Electronic Imaging Conference - 01/22/04
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15. Grayscale design algorithm
4/25/2017
Grayscale design algorithm
Existing minority dots from level G1
Generate parametric linear filter FG2 for level G2
Filter existing dot pattern for level G1 using FG2 and circular convolution
Find majority pixel where the result is minimum and convert it to a minority pixel
Add shifted version of FG2
to earlier filtered result
Desired concentration
of minority pixels? No
stop
Yes
SPIE Electronic Imaging Conference - 01/22/04
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16. Optimal Multifilters for color screens
4/25/2017
Optimal Multifilters for color screens
SPIE Electronic Imaging Conference - 01/22/04
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17. Optimal Multifilters for color screens
4/25/2017
Optimal Multifilters for color screens
SPIE Electronic Imaging Conference - 01/22/04
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18. Color design algorithm
4/25/2017
Color design algorithm
Add circularly shifted versions of impulse response vector (for impulses in C and M) of FG2 and add to earlier filtered vector result for C,M
Existing minority dots from level G1 in C and M
Generate parametric matrix-valued linear filter FG2
Filter existing dot pattern for level
G1 using matrix-valued filter FG2.
Find majority pixel in C, M where the result is minimum and convert it them to a minority pixel
Desired concentration
of minority pixels? No
stop
Yes
SPIE Electronic Imaging Conference - 01/22/04
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19. SPIE Electronic Imaging Conference - 01/22/04
4/25/2017
Results
C,M=62/255
SPIE Electronic Imaging Conference - 01/22/04
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20. Results (epxand images to see dot structure)
4/25/2017
Results (epxand images to see dot structure)
Optimized donut filter
Gaussian donut filter [Lin]
SPIE Electronic Imaging Conference - 01/22/04
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21. 4/25/2017
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