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7





             Characterization of Computer

             Displays





             7.1   Introduction

             Linear transforms are fundamental to the study of colorimetry and have many
             applications, especially in the characterization of imaging devices such as
             monitors, cameras and printers. Device calibration is concerned with setting the
             imaging device to a known state and ensures that the device is producing
             consistent results. Characterization is the relationship between device coordi-
             nates (usually RGB or CMYK) and some device-independent colour space such
             as CIE XYZ (Fairchild, 1998; Johnson, 2002). Green (2002b) argues that there
             are three main methods for achieving this mapping: physical models, look-up
             tables and numerical methods. Physical models often include terms for various
             properties of the device such as the absorbance, scattering and reflectance of
             colorants. The Kubelka–Munk model is an example of a physical model that can
             be used as the basis of a characterization method for a printer (Kang, 1994;
             Johnson, 1996). Similarly, the gain–offset–gamma model (also known as GOG)
             is a physical model of a computer- or visual-display unit based on a cathode-ray
             tube (CRT) that can be used for the characterization of most display monitors.
             Look-up tables define the mapping between a device space and a CIE colour
             space at a series of discrete measured coordinates within the colour space and
             may interpolate the values for intermediate coordinates. For numerical methods
             a series of coefficients is determined, usually based upon a set of measured
             samples, without prior assumptions about the physical behaviour of the device or
             its associated media. Examples of numerical methods include linear transforms,
             non-linear transforms or polynomials and artificial neural networks. A key
             property of any transform is whether it can easily be inverted. The advantage of
             a linear transform is that it is trivial to invert whereas many empirical models are
             not easily inverted (Iino and Berns, 1998). If inversion is not possible, then



             Computational Colour Science Using MATLAB. By Stephen Westland and Caterina Ripamonti.
             & 2004 John Wiley & Sons, Ltd: ISBN 0 470 84562 7
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