Contrast-Aware Halftoning

Contrast-Aware Halftoning

Hua Li and David Mould

April 20, 2023 1

Previous Work

April 20, 2023 2

Original Image Floyd-Steinberg error diffusion[FS74]

Tone Reproduction

Visual artifacts

Lack of structure preservation

Previous Work

April 20, 2023 3

Ostromoukhov’s method[Ost01]

Tone ReproductionBlue Noise

Floyd-Steinberg error diffusion[FS74]

Visual artifacts

Lack of structure preservationLack of structure preservation

Improved

Previous Work

April 20, 2023 4

Ostromoukhov’s method[Ost01] Structure-aware halftoning[Pang et al. 2008]

Blue Noise Structure Preservation

Structure preservation

Very slow

Lack of structure preservation

Previous Work--Current Art of State

April 20, 2023 5

Structure-aware error diffusion[Chang et al. 2009]

Structure PreservationStructure Preservation

Structure preservation

Very fast but a little lower quality in structure preservation

Structure preservation

Very slow

Structure-aware halftoning[Pang et al. 2008]

Comparison with Our Work

April 20, 2023 6

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

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 20, 2023 7

Observations for Contrast Enhancement

April 20, 2023 8Artists’ work

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 20, 2023 9

1. Our Basic Algorithm

• Basically, our basic method is an extension to Floyd-Steinberg error diffusion.– Pixel by pixel

April 20, 2023 10

Contrast-aware mask

p(i,j)

1. Our Basic Algorithm

April 20, 2023 11

1. 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

Contrast-preserved Error Distribution

April 20, 2023 12

255

0

0

128

255

128

<128

0

The center pixel The center pixel

Positive error

>128Negative error

255

p(i,j)

Nearby pixels Lightened

Nearby pixels Darkened

Uniform Region

Contrast-preserved Error Distribution

April 20, 2023 13

255

255

0

0

Positive error

Negative error

Original After

Non-uniform Region

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 20, 2023 14

Comparisons for Ramp

April 20, 2023 15

Ostromoukhov’s method

Structure-aware halftoning

Our basic method(Have annoying patterns)

Floyd-Steinberg error diffusion

Ramp

2. Our Variant Method

• Instead of the raster scanning order, dynamically priority-based scheme– Closer to either extreme(black or white), higher

priority.

April 20, 2023 16

Contrast-preserved Error Distribution

April 20, 2023 17

255

0

0

128

255

128

<128

0

The center pixel The center pixel

Positive error

>128Negative error

255

p(i,j)

Uniform Region

Highest priority

Highest priority

Highest priority

Highest priority

Lowered

Lowered

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 20, 2023 18

Visualize the Orders after Our Variant method

April 20, 2023 19

Visualize the orders for the tree image. - The first pixel is set as black and the last pixel is set as white.

Comparisons for Ramp

April 20, 2023 20

Our basic method(Have annoying patterns)

Our variant method

Improvement for Mid-tone

April 20, 2023 21

Ostromoukhov’s method

Structure-aware halftoning

Our variant method

Floyd-Steinberg error diffusion

Ramp intensity

Part of Tree

April 20, 2023 22

(a)Structure-aware halftoning (b)Structure-aware error diffusion

(c)Our basic method (d)Our variant method

Snail

April 20, 2023 23

April 20, 202324

Structure-aware halftoning

Structure-aware error diffusion

Our basic method

Our variant method

Comparisons(1)

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April 20, 2023 26

SAH

SAED

Basic

Variant

Comparisons(2)

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April 20, 2023 28

Comparisons(4)

April 20, 2023 29Structure-aware halftoning Our basic method Our variant method

Evaluation for Structure Similarity

April 20, 2023 30MSSIM(the mean structural similarity measure[Wang et al. 2004])

EvaluationTone Similarity and Structure Similarity

April 20, 202331

The peak signal-to-noise ratio(PSNR)

MSSIM

Evaluation-Contrast Similarity

April 20, 2023 32

the peak signal-to-noise ratio based on local contrast image(CPSNR)

Blue Noise Properties by the Radially Averaged Power Spectrum

April 20, 2023 33

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

Analysis

• CPU Timing(Process a 512 ×512 image)

• Limitation: not optimal; sometimes clumping happens.

April 20, 2023 34

Methods Structure-aware halftoning

Structure-aware error diffusion(16×16 mask)*

Our basic method(7×7 mask)**

Our variant method(7×7 mask)**

Time 2 minutes 6.74 seconds 0.492 seconds 2.955 seconds

* Best tradeoff between quality and speed** Similar hardware conditions as SAED

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.

April 20, 2023 35

Contributions

• Based on error diffusion, propose contrast-aware methods for halftoning creation.

• Introduce dynamically priority-based scheme into halftoning.

April 20, 2023 36

Future Work

• Shape influences• Other image features to adjust local contrast• Color halftoning• Other artistic styles through pixel

management

April 20, 2023 37

Acknowledgement

• Thanks to:

Grants from NSERC and Carleton University

April 20, 2023 38

More Results:Based on Our Variant Method

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