Optimizing the Error Diffusion Filter for Blue Noise Halftoning With Multiscale Error Diffusion

Fung, Yik-Hing; Chan, Yuk‐Hee

doi:10.1109/tip.2012.2211370

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…Li and Mould [22] alleviated contrast varying problems using contrast-aware masks. Recently, Fung and Chan [12] suggested a method to optimize diffusion filters to provide blue-noise characteristics on multiscale halftoning, and Guo et al [13] proposed a tone-replacement method to reduce banding and noise artifacts.…”

Section: Dithering and Error Diffusionmentioning

confidence: 99%

GIFnets: Differentiable GIF Encoding Framework

Yoo¹,

Luo²,

Wang³

et al. 2020

Preprint

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Graphics Interchange Format (GIF) is a widely used image file format. Due to the limited number of palette colors, GIF encoding often introduces color banding artifacts. Traditionally, dithering is applied to reduce color banding, but introducing dotted-pattern artifacts. To reduce artifacts and provide a better and more efficient GIF encoding, we introduce a differentiable GIF encoding pipeline, which includes three novel neural networks: PaletteNet, DitherNet, and BandingNet. Each of these three networks provides an important functionality within the GIF encoding pipeline. PaletteNet predicts a near-optimal color palette given an input image. DitherNet manipulates the input image to reduce color banding artifacts and provides an alternative to traditional dithering. Finally, BandingNet is designed to detect color banding, and provides a new perceptual loss specifically for GIF images. As far as we know, this is the first fully differentiable GIF encoding pipeline based on deep neural networks and compatible with existing GIF decoders. User study shows that our algorithm is better than Floyd-Steinberg based GIF encoding.

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Section: Dithering and Error Diffusionmentioning

confidence: 99%

GIFnets: Differentiable GIF Encoding Framework

Yoo¹,

Luo²,

Wang³

et al. 2020

Preprint

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“…Since previous studies (Chan & Cheung, 2004;Fung & Chan, 2010;Fung & Chan, 2013;Katsavounidis & Kuo, 1997) indicate that MED gives better performance compared to traditional error diffusion, to perform fair comparisons, we will measure the differences between the amended stego halftone images and the cover halftone images. Here, Human-visual Peak Signal-to-Noise Ratio (HPSNR) (Guo & Liu, 2009) will be employed to measure the qualities of the halftone images.…”

Section: Figure 3 Flowchart Of Cop-medmentioning

confidence: 99%

Watermark Embedding for Multiscale Error Diffused Halftone Images by Adopting Visual Cryptography

Guo

Tang

2015

International Journal of Digital Crime and Forensics

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Error Diffusion has been widely adopted in the printing industry due to its good visual quality and simple implementation. However, error diffusion still possesses its own deficiencies. Thus multiscale error diffusion (MED) has been developed, and this method outperforms traditional error diffusion according to extensive research results. The majority of previous halftone image watermarking techniques cannot be directly applied to MED halftone images. Since there is no halftone visual watermarking (HVW) method for MED halftone images in existing methods, we propose the first HVW method for MED halftone images, Copyright Protecting Multiscale Error Diffusion (CoP-MED), in this paper. By adopting the visual cryptography principle, CoP-MED can effectively embed a secret pattern into two MED halftone images, where the secret pattern can be decoded clearly by simply overlaying the two stego halftone images or performing not-exclusive-or operation between them. Parameter selection is also discussed based on the experimental results. Later, in comparison tests, CoP-MED shows superior performance compared to existing works.

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“…30,[32][33][34][35][36][37] Without loss of generality, consider the case that we are handling layer I n . Layer I 1 is handled first because it carries more energy.…”

Section: Details Of Stagementioning

confidence: 99%

“…33 As for the adjustment of A k for k ≠ n, a tone-dependent diffusion filter is used instead. It is necessary to adjust the transient intensity planes of all layers with error diffusion.…”

Section: Details Of Stagementioning

confidence: 99%

“…31 Extensive studies on FMED show that it is able to eliminate directional hysteresis, preserve spatial features, and provide outputs with good blue noise characteristics. [32][33][34][35][36][37] During its realization, pixels are not processed sequentially with a predefined order, which allows one to effectively and efficiently control dot positioning. Because controlling color overlapping, dot positioning, and dot coloring is the key to success in HED, we would like to explore whether fusing HED and FMED can create greater synergy in color halftoning.…”

Section: Introductionmentioning

confidence: 99%

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Blue noise digital color halftoning with multiscale error diffusion

Fung

Chan

2014

J. Electron. Imaging

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A recent trend in color halftoning is to explicitly control color overlapping, dot positioning, and dot coloring in different stages of the halftoning process. As feature-preserving multiscale error diffusion (FMED) shows its capability and flexibility in dot positioning in both binary and multilevel halftoning applications, it naturally becomes a potential candidate in the development of new color halftoning algorithms. This paper presents an FMED-based color halftoning algorithm developed based on the aforementioned strategy. In contrast to the algorithms that adopt the same strategy, the proposed algorithm has no bias for specific Neugebauer primaries in dot coloring and has no fixed scanning path to place dots in dot positioning. These features allow the algorithm to place dots of the correct colors on the right positions with less constraint. Hence, the algorithm is better able to preserve image features. Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Journal of Electronic Imaging 063013-10 Nov∕Dec 2014 • Vol. 23(6) Fung and Chan: Blue noise digital color halftoning with multiscale error diffusion Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms

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Optimizing the Error Diffusion Filter for Blue Noise Halftoning With Multiscale Error Diffusion

Cited by 17 publications

References 8 publications

GIFnets: Differentiable GIF Encoding Framework

GIFnets: Differentiable GIF Encoding Framework

Watermark Embedding for Multiscale Error Diffused Halftone Images by Adopting Visual Cryptography

Blue noise digital color halftoning with multiscale error diffusion

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