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Contrast-Aware Halftoning Hua Li and David Mould April 22, 20151.

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Presentation on theme: "Contrast-Aware Halftoning Hua Li and David Mould April 22, 20151."— Presentation transcript:

1 Contrast-Aware Halftoning Hua Li and David Mould April 22, 20151

2 Previous Work April 22, Original Image Floyd-Steinberg error diffusion[FS74] Tone Reproduction Visual artifacts Lack of structure preservation

3 Previous Work April 22, Ostromoukhov’s method[Ost01] Tone Reproduction Blue Noise Floyd-Steinberg error diffusion[FS74] Visual artifacts Lack of structure preservation Improved

4 Previous Work April 22, Ostromoukhov’s method[Ost01] Structure-aware halftoning[Pang et al. 2008] Blue Noise Structure Preservation Structure preservation Very slow Lack of structure preservation

5 Previous Work--Current Art of State April 22, Structure-aware error diffusion[Chang et al. 2009] Structure Preservation Structure preservation Very fast but a little lower quality in structure preservation Structure preservation Very slow Structure-aware halftoning[Pang et al. 2008]

6 Comparison with Our Work April 22, 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. April 22, 20157

8 Observations for Contrast Enhancement April 22, 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 ? April 22, 20159

10 1. Our Basic Algorithm Basically, our basic method is an extension to Floyd- Steinberg error diffusion. – Pixel by pixel April 22, Contrast-aware mask p(i,j)

11 1. Our Basic Algorithm April 22, Determine the pixel color: (closer to black) or (closer to white); 2.Calculate the error(the difference): the original intensity - the chosen intensity; 3.Calculate the weights of contrast-sensitive mask; 4.Normalize the weights; 5.Diffuse the error. For each pixel p(i,j) Based on FS error diffusion

12 Contrast-preserved Error Distribution April 22, <128 0 The center pixel Positive error >128 Negative error 255 p(i,j) Nearby pixels Lightened Nearby pixels Darkened Uniform Region

13 Contrast-preserved Error Distribution April 22, Positive error Negative error OriginalAfter 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 April 22,

15 Comparisons for Ramp April 22, Ostromoukhov’s method Structure-aware halftoning Our basic method (Have annoying patterns) Floyd-Steinberg error diffusion Ramp

16 2. Our Variant Method Instead of the raster scanning order, dynamically priority-based scheme – Closer to either extreme(black or white), higher priority. April 22,

17 Contrast-preserved Error Distribution April 22, <128 0 The center pixel Positive error >128 Negative error 255 p(i,j) Uniform Region Highest priority Lowered

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. April 22,

19 Visualize the Orders after Our Variant method April 22, 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 April 22, Our basic method (Have annoying patterns) Our variant method

21 Improvement for Mid-tone April 22, Ostromoukhov’s method Structure-aware halftoning Our variant method Floyd-Steinberg error diffusion Ramp intensity

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

23 Snail April 22,

24 April 22, Structure-aware halftoning Structure-aware error diffusion Our basic method Our variant method

25 Comparisons(1) April 22,

26 April 22, SAH SAED Basic Variant

27 Comparisons(2) April 22,

28 April 22,

29 Comparisons(4) April 22, Structure-aware halftoning Our basic methodOur variant method

30 Evaluation for Structure Similarity April 22, MSSIM( the mean structural similarity measure[Wang et al. 2004])

31 Evaluation Tone Similarity and Structure Similarity April 22, The peak signal-to- noise ratio( PSNR) MSSIM

32 Evaluation-Contrast Similarity April 22, the peak signal-to-noise ratio based on local contrast image( CPSNR)

33 Blue Noise Properties by the Radially Averaged Power Spectrum April 22, Grayness = 0.82Our basic method and its RAPSD Our variant method and its RAPSD Our variant method with tie-breaking and its RAPSD Structure-aware method and its RAPSD

34 Analysis CPU Timing(Process a 512 ×512 image) Limitation: not optimal; sometimes clumping happens. April 22, MethodsStructure-aware halftoning Structure-aware error diffusion (16×16 mask)* Our basic method (7×7 mask)** Our variant method (7×7 mask)** Time2 minutes6.74 seconds0.492 seconds2.955 seconds * Best tradeoff between quality and speed ** Similar hardware conditions as SAED

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. April 22,

36 Contributions Based on error diffusion, propose contrast- aware methods for halftoning creation. Introduce dynamically priority-based scheme into halftoning. April 22,

37 Future Work Shape influences Other image features to adjust local contrast Color halftoning Other artistic styles through pixel management April 22,

38 Acknowledgement Thanks to: Grants from NSERC and Carleton University April 22,

39 More Results: Based on Our Variant Method April 22,

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