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Benoît Alain

dblp:13/6665 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video processing · 67% Visualization and visual analytics · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › halftoning
error diffusion
0.112009
Structure-aware error diffusion · ACM Trans. Graph. 2009
Image and video processing
halftoning
0.112009
Structure-aware error diffusion · ACM Trans. Graph. 2009
Visualization and visual analytics › data visualization
image visualization
0.112009
Structure-aware error diffusion · ACM Trans. Graph. 2009

Methods — techniques the papers use, named apart from their topics

threshold modulation · 0.1frequency content analysis · 0.1error-diffusion coefficient variation · 0.1
YearPublicationVenuePosition
2009 Structure-aware error diffusion
abstract
We present an original error-diffusion method which produces visually pleasant halftone images while preserving fine details and visually identifiable structures present in the original images. Our method is conceptually simple and computationally efficient. The source image is analyzed, and its local frequency content is detected. The main component of the frequency content (main frequency, orientation and contrast) serve as lookup table indices to a pre-calculated database of modifications to a standard error diffusion. The modifications comprise threshold modulation and variation of error-diffusion coefficients. The whole system is calibrated in such a way that the produced halftone images are visually close to the original images (patches of constant intensity, patches containing sinusoidal waves of different frequencies/orientations/contrasts, as well as natural images of different origins). Our system produces images of visual quality comparable to that presented in [Pang et al. 2008], but much faster. When processing typical images of linear size of several hundreds of pixels, our error-diffusion system is two to three orders of magnitude faster than [Pang et al. 2008]. Thanks to its speed combined with high visual quality, our error-diffusion algorithm can be used in many practical applications which may require digital halftoning: printing, visualization, geometry processing, various sampling techniques, etc.
Jianghao Chang, Benoît Alain, Victor Ostromoukhov
ACM Trans. Graph.2