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Proceedings Paper

The use of spatially based complexity measures towards color gamut mapping and image resizing
Author(s): Vishal Monga; Raja Bala; Claude Fillion
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Paper Abstract

Several color-imaging algorithms such as color gamut mapping to a target device and resizing of color images have traditionally involved pixel-wise operations. That is, each color value is processed independent of its neighbors in the image. In recent years, applications such as spatial gamut mapping have demonstrated the virtues of incorporating spatial context into color processing tasks. In this paper, we investigate the use of locally based measures of image complexity such as the entropy to enhance the performance of two color imaging algorithms viz. spatial gamut mapping and content-aware resizing of color images. When applied to spatial gamut mapping (SGM), the use of these spatially based local complexity measures helps adaptively determine gamut mapping parameters as a function of image content - hence eliminating certain artifacts commonly encountered in SGM algorithms. Likewise, developing measures of complexity of color-content in a pixel neighborhood can help significantly enhance performance of content-aware resizing algorithms for color images. While the paper successfully employs intuitively based measures of image complexity, it also aims to bring to light potentially greater rewards that may be reaped should more formal measures of local complexity of color content be developed.

Paper Details

Date Published: 19 January 2010
PDF: 10 pages
Proc. SPIE 7528, Color Imaging XV: Displaying, Processing, Hardcopy, and Applications, 75280H (19 January 2010); doi: 10.1117/12.843246
Show Author Affiliations
Vishal Monga, The Pennsylvania State Univ. (United States)
Raja Bala, Xerox Corp. (United States)
Claude Fillion, Xerox Corp. (United States)


Published in SPIE Proceedings Vol. 7528:
Color Imaging XV: Displaying, Processing, Hardcopy, and Applications
Reiner Eschbach; Gabriel G. Marcu; Shoji Tominaga; Alessandro Rizzi, Editor(s)

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