Contrast-Aware Halftoning

1. Contrast-Aware Halftoning Hua Li and David Mould

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

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]

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

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]

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

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.

8. Observations for Contrast Enhancement 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 ?

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

11. 1. Our Basic Algorithm 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

12. Contrast-preserved Error Distribution The center pixel The center pixel 255 Positive error 128 Nearby pixels Lightened <128 0 0 p(i,j) 255 255 >128 Negative error 128 Nearby pixels Darkened 0 Uniform Region

13. Contrast-preserved Error Distribution Positiveerror 255 0 Original After Negative error 255 0 Non-uniform Region

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

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

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

17. Contrast-preserved Error Distribution The center pixel The center pixel 255 Highest priority Positive error 128 Lowered <128 0 Highest priority 0 p(i,j) 255 255 Highest priority >128 Negative error 128 Lowered Highest priority 0 Uniform Region

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.

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.

20. Comparisons for Ramp Our basic method (Have annoying patterns) Our variant method

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

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

23. Snail

24. Structure-aware halftoning Structure-aware error diffusion Our basic method Our variant method

25. Comparisons(1)

26. SAH SAED Basic Variant

27. Comparisons(2)

29. Comparisons(4) Structure-aware halftoning Our basic method Our variant method

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

31. EvaluationTone Similarity and Structure Similarity The peak signal-to-noise ratio(PSNR) MSSIM

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

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

34. Analysis • CPU Timing(Process a 512 ×512 image) • Limitation: not optimal; sometimes clumping happens. * Best tradeoff between quality and speed ** Similar hardware conditions as SAED

35. Summary • We have a tradeoff of intensity fidelityvs. 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.

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

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

38. Acknowledgement • Thanks to: Grants from NSERC and Carleton University

39. More Results:Based on Our Variant Method