Novel Colour Printer Characterization Model

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s):
  • Rendering and Modeling
• Date: July 2003
• ISSN: 1017-9909
• Journal: Journal of Electronic Imaging
• Number: 3
• Volume: 12
• Pages: 448 – 458
• Keywords: neural network, Colorimetric characterization, Radial basis function

Abstract

A key problem in multimedia systems is the faithful reproduction of color. One of the main reasons why this is a complicated issue are the different color reproduction technologies used by the various devices; displays use easily modeled additive color mixing, while printers use a subtractive process, the characterization of which is much more complex than that of self--luminous displays.

In order to resolve these problems several processing steps are necessary, one of which is accurate device characterization. Our study examines different learning algorithms for one particular neural network technique which already has been found to be useful in related contexts -- namely radial basis function network models -- and proposes a modified learning algorithm which improves the colorimetric characterization process of printers.

In particular our results show that is possible to obtain good performance by using a learning algorithm that is trained on only small sets of color samples, and use it to generate a larger look--up table (LUT) through use of multiple polynomial regression or an interpolation algorithm. We deem our findings to be a good start point for further studies on learning algorithms used in conjunction with this problem.

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BibTeX

@article{Artusi-2003-Col,
  title =      "Novel Colour Printer Characterization Model",
  author =     "Alessandro Artusi and Alexander Wilkie",
  year =       "2003",
  abstract =   "A key problem in multimedia systems is the faithful
               reproduction of color. One of the main reasons why this is a
               complicated issue are the different color reproduction
               technologies used by the various devices; displays use
               easily modeled additive color mixing, while printers use a
               subtractive process, the  characterization of which is much
               more complex than that of self--luminous displays.  In order
               to resolve these problems several processing steps are
               necessary, one of which is accurate device characterization.
               Our study examines different learning algorithms for one
               particular neural network technique which already has been
               found to be useful in related contexts -- namely radial
               basis function network models -- and proposes a modified
               learning algorithm which improves the colorimetric
               characterization process of printers.  In particular our
               results show that is possible to obtain good performance by
               using a learning algorithm that is trained on only small
               sets of color samples, and use it to generate a larger
               look--up table (LUT) through use of multiple polynomial
               regression or an interpolation algorithm. We deem our
               findings to be a good start point for further studies on
               learning algorithms used in conjunction with this problem.",
  month =      jul,
  issn =       "1017-9909",
  journal =    "Journal of Electronic Imaging",
  number =     "3",
  volume =     "12",
  pages =      "448--458",
  keywords =   "neural network, Colorimetric characterization, Radial basis
               function",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2003/Artusi-2003-Col/",
}