Linear color-separable human visual system models for vector error diffusion halftoning

Image halftoning converts a high-resolution image to a low-resolution image, e.g., a 24-bit color image to a three-bit color image, for printing and display. Vector error diffusion captures correlation among color planes by using an error filter with matrix-valued coefficients. In optimizing vector...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 10; no. 4; pp. 93 - 97
Main Authors Monga, V., Geisler, W.S., Evans, B.L.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2003
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Color
Diffusion
Displays
Errors
Filters
Humans
Image converters
Image processing
Mathematical analysis
Matrix converters
Optimization
Printing
Vectors
Vectors (mathematics)
Visual
Visual system
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Summary:Image halftoning converts a high-resolution image to a low-resolution image, e.g., a 24-bit color image to a three-bit color image, for printing and display. Vector error diffusion captures correlation among color planes by using an error filter with matrix-valued coefficients. In optimizing vector error filters, Damera-Venkata and Evans (see IEEE Trans. Image Processing, vol.10, p.1552-65, Oct. 2001) transform the error image into an opponent color space where Euclidean distance has perceptual meaning. This letter evaluates color spaces for vector error filter optimization. In order of increasing quality, the color spaces are YIQ, YUV, opponent (by Poirson and Wandell, 1993), and linearized CIELab (by Flohr, Kolpatzik, Balasubramanian, Carrara, Bouman, and Allebach, 1993).
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2002.806708