1. Contrast-Aware Halftoning
Hua Li and David Mould
December 2, 2018

2. Previous Work Tone Reproduction Floyd-Steinberg error diffusion[FS74]
Visual artifacts
Lack of structure preservation
Floyd-Steinberg error diffusion[FS74]
Original Image
December 2, 2018

3. Previous Work Tone Reproduction Blue Noise
Improved
Visual artifacts
Lack of structure preservation
Lack of structure preservation
Floyd-Steinberg error diffusion[FS74]
Ostromoukhov’s method[Ost01]
December 2, 2018

4. Previous Work Structure Preservation Blue Noise Very slow
Lack of structure preservation
Structure preservation
Very slow
Structure-aware halftoning[Pang et al. 2008]
Ostromoukhov’s method[Ost01]
December 2, 2018

5. Previous Work--Current Art of State
Structure Preservation
Structure Preservation
Structure preservation
Structure preservation
Very fast but a little lower quality in structure preservation
Very slow
Structure-aware halftoning[Pang et al. 2008]
Structure-aware error diffusion[Chang et al. 2009]
December 2, 2018

6. Comparison with Our Work
Contrast-aware halftoning(Our variant method)
Structure-aware halftoning[Pang et al. 2008]
December 2, 2018

7. Motivation Human perception is sensitive to contrast.
Visual effect/impression more important than tone matching.
Observation(at the core of our algorithm)
Using more black pixels in the dark side and fewer black pixels on the light side will promote the local contrast.
December 2, 2018

8. Observations for Contrast Enhancement
December 2, 2018
Artists’ work

9. Goal and Problem
Goal: Structure preservation without loss of tone quality and sacrificing speed
Problem:
How to cluster black pixels in white area to maintain local contrast for generating structure-preserved monochrome halftoning ?
December 2, 2018

10. 1. Our Basic Algorithm
Basically, our basic method is an extension to Floyd-Steinberg error diffusion.
Pixel by pixel
p(i,j)
Contrast-aware mask
December 2, 2018

11. 1. Our Basic Algorithm For each pixel
p(i,j)
For each pixel
Determine the pixel color: (closer to black) or (closer to white);
Calculate the error(the difference): the original intensity － the chosen intensity;
Calculate the weights of contrast-sensitive mask;
Normalize the weights;
Diffuse the error.
Based on FS error diffusion
December 2, 2018

12. Contrast-preserved Error Distribution
The center pixel
The center pixel
255
Positive error
128
Nearby pixels Lightened
<128
p(i,j)
255
255
>128
Negative error
128
Nearby pixels Darkened
Uniform Region
December 2, 2018

13. Contrast-preserved Error Distribution
Positive error
255
Original
After
Negative error
255
Non-uniform Region
December 2, 2018

14. Contrast-preserved Error Distribution
Contrast-sensitive circular mask
Maintain the initial tendency that darker pixels should be more likely to be set to black while lighter pixels should be more likely to be set to white.
The nearby darker pixels absorb less positive error and the lighter pixels absorb more.
Conversely, negative error is distributed preferentially to dark pixels, making them even darker.
Weights steeply dropping off from center
Normalized
December 2, 2018

15. Comparisons for Ramp Ramp Floyd-Steinberg error diffusion
Ostromoukhov’s method
Structure-aware halftoning
Our basic method
(Have annoying patterns)
December 2, 2018

16. 2. Our Variant Method
Instead of the raster scanning order, dynamically priority-based scheme
Closer to either extreme(black or white), higher priority.
December 2, 2018

17. Contrast-preserved Error Distribution
The center pixel
The center pixel
255
Highest priority
Positive error
128
Lowered
<128
Highest priority
p(i,j)
255
255
Highest priority
>128
Negative error
128
Lowered
Highest priority
Uniform Region
December 2, 2018

18. Priority-based Scheme
The neighboring pixels change priorities after using contrast aware mask.
The neighboring pixels will not be chosen as the next pixel. To guarantee a better spatial distribution.
An up-to-date local priority order, empirically, results in superior detail preservation.
December 2, 2018

19. Visualize the Orders after Our Variant method
Visualize the orders for the tree image.
- The first pixel is set as black and the last pixel is set as white.
December 2, 2018

20. Comparisons for Ramp Our basic method (Have annoying patterns)
Our variant method
December 2, 2018

21. Improvement for Mid-tone
Ramp intensity
Floyd-Steinberg
error diffusion
Ostromoukhov’s method
Structure-aware halftoning
Our variant method
December 2, 2018

22. Part of Tree (a)Structure-aware halftoning
(b)Structure-aware error diffusion
(c)Our basic method
(d)Our variant method
December 2, 2018

23. Snail
December 2, 2018

24. Structure-aware halftoning
Structure-aware error diffusion
Our basic method
Our variant method
December 2, 2018

25. Comparisons(1)
December 2, 2018

26. SAH
SAED
Basic
Variant
December 2, 2018

27. Comparisons(2)
December 2, 2018

28. December 2, 2018

29. Comparisons(4) Structure-aware halftoning Our basic method
December 2, 2018
Our basic method
Our variant method

30. Evaluation for Structure Similarity
MSSIM(the mean structural similarity measure[Wang et al. 2004])
December 2, 2018

31. Evaluation Tone Similarity and Structure Similarity
The peak signal-to-noise ratio(PSNR)
MSSIM
December 2, 2018

32. Evaluation-Contrast Similarity
the peak signal-to-noise ratio based on local contrast image(CPSNR)
December 2, 2018

33. Blue Noise Properties by the Radially Averaged Power Spectrum
Our basic method and its RAPSD
Grayness = 0.82
Our variant method and its RAPSD
Structure-aware method and its RAPSD
Our variant method with tie-breaking and its RAPSD
December 2, 2018

34. Analysis CPU Timing(Process a 512 ×512 image)
Limitation: not optimal; sometimes clumping happens.
Methods
Structure-aware halftoning
Structure-aware error diffusion
(16×16 mask)*
Our basic method
(7×7 mask)**
Our variant method
Time
2 minutes
6.74 seconds
0.492 seconds
2.955 seconds
* Best tradeoff between quality and speed
** Similar hardware conditions as SAED
December 2, 2018

35. Summary
We have a tradeoff of intensity fidelity vs. structural fidelity and have the best structure preservation of any reported results to date.
Contrast-aware halftoning is automatic, easy to implement, and fast.
Contrast is an important factor.
December 2, 2018

36. Contributions
Based on error diffusion, propose contrast-aware methods for halftoning creation.
Introduce dynamically priority-based scheme into halftoning.
December 2, 2018

37. Future Work Shape influences
Other image features to adjust local contrast
Color halftoning
Other artistic styles through pixel management
December 2, 2018

38. Acknowledgement Thanks to: Grants from NSERC and Carleton University
December 2, 2018

39. More Results: Based on Our Variant Method
December 2, 2018

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