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Content Provider | IEEE Xplore Digital Library |
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Author | Nae-Joung Kwak Sung-Ja Choi Gang-Su Lee |
Copyright Year | 2006 |
Description | Author affiliation: Dept. of Inf. Commun. Eng., Mokwon Univ. (Nae-Joung Kwak) |
Abstract | Among the digital halftoning, the error diffusion halftoning gives better subjective quality than other halftoning techniques. But it also makes edges of objects blurred. To overcome the defect, this paper proposes the modified error diffusion halftoning algorithm to enhance the edges using the spatial perceptual characteristics of the human visual system. Using the characteristics that the human vision perceives not the pixel's luminance itself but the local average luminance and that information that human eyes perceive spatial variation, the proposed method computes edge enhancement information (EEI). The EEI is added to the quantizer's input pixel and feeds into the halftoning quantizer. The quantizer produces the halftone image having the enhanced edge. Also this paper proposes the technique that the coefficients of the error diffusion filter are adapted according to the correlation among color components. The computer simulation results show that the proposed method produces finer halftoning images than conventional methods due to the enhanced edges. And the proposed method also preserves similar in edges to original image and reduces some defects such as color impulse and false contours |
Sponsorship | IEEE Comput. Soc |
Starting Page | 551 |
Ending Page | 557 |
File Size | 797773 |
Page Count | 7 |
File Format | |
ISBN | 0769526748 |
DOI | 10.1109/ICHIT.2006.253543 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2006-11-09 |
Publisher Place | South Korea |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Visual system Computer vision Eyes Humans Color Computer errors Quantization Information filters Information filtering Feeds |
Content Type | Text |
Resource Type | Article |
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