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A grid approach to optimizing color recipes

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We show that the accuracy of predicting color recipes for solid colors using the conventional Kubelka-Munk model can be improved by using grid-based empirical techniques.

We first identify the regions in color space where such improvements would be most useful. These turn out to be the dark red, dark blue and the off-white regions. In these regions of color space, we create grids using several different methods: Delaunay triangulation and Adaptive meshing techniques with thresholds either on non-linearity or on fixed distances between grid points. This last method was shown to work best for this application.

In order to match an arbitrary point in color space, based on the created grid, two different interpolation methods were tested: Linear optimization (where a linear relation between concentration space and color space is assumed within each grid cell) and Local K and S determination (where values of the Kubelka-Munk parameters are allowed to vary over color space). Our results show that grid methods using Local K and S determination lead to a significant improvement in accuracy as compared to conventional Kubelka-Munk methods.
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Document Type: Research Article

Publication date: January 1, 2012

More about this publication?
  • Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual papers for details.

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