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The Color Logarithmic Image Processing (CoLIP) Antagonist Space

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Color Image and Video Enhancement

Abstract

The color logarithmic image processing (CoLIP) is a mathematical framework for the representation and processing of color images. It is psychophysically well justified since it is consistent with several human visual perception laws and characteristics. It is mathematically and computationally relevant since it allows to consider color images as vectors in an abstract linear space, contrary to the classical color spaces (e.g., RGB and \(L^*a^*b^*\)). The first purpose of this chapter is to present the mathematical fundamentals of the CoLIP together with its main psychophysical connections (Grasmann’s law, color matching functions, chromaticity diagram, and the Maxwell triangle). The second purpose is to present some basic image processing and analysis techniques for contrast enhancement (histogram equalization, dynamic range maximization, and toggle contrast calculation), white balance correction, color transfer, K-means clustering, and filtering. Most of them are applied on various original color images in a comparative way between CoLIP, RGB, and \(L^*a^*b^*\) color spaces.

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Notes

  1. 1.

    by Jerker Wågberg, More Research and DPC, www.more.se.

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Authors and Affiliations

  1. École Nationale Supérieure des Mines, Saint-Etienne, France

    Yann Gavet, Johan Debayle & Jean-Charles Pinoli

Authors
  1. Yann Gavet
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  2. Johan Debayle
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  3. Jean-Charles Pinoli
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Correspondence to Yann Gavet .

Editor information

Editors and Affiliations

  1. Computer Science dept., Louisiana State University Shreveport, Shreveport, Louisiana, USA

    Emre Celebi

  2. Fondazione Bruno Kessler Center for Information and Communication Technology, Trento, Italy

    Michela Lecca

  3. Silesian University of Technology, Gliwice, Poland

    Bogdan Smolka

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Gavet, Y., Debayle, J., Pinoli, JC. (2015). The Color Logarithmic Image Processing (CoLIP) Antagonist Space. In: Celebi, E., Lecca, M., Smolka, B. (eds) Color Image and Video Enhancement. Springer, Cham. https://doi.org/10.1007/978-3-319-09363-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-09363-5_6

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