Page 135 - Computational Colour Science Using MATLAB

122 CHARACTERIZATION OF COMPUTER DISPLAYS

Box 19: compgog.m

function [rgb] = compgog(gogs,dacs)

% function [rgb] = compgog(gogs,dacs)
% computes the linearized RGB values
% from the normalized RGB values
% for a given set of gog values
% gog is a 2 by 1 matrix that contains the gamma and gain
% dacs is an n by 1 matrix that contains the RGB values
% rgb is an n by 1 matrix of linearized RGB values

gamma = gogs(1);
gain = gogs(2);
for i = 1:length(dacs)
if (gain*dacs(i) + (1-gain)) 5=0
rgb(i) = 0;
else
rgb(i) = (gain*dacs(i) + (1-gain))^gamma;
end
end
% force output to be a column vector
rgb = rgb(:);

The characterization performance for the eight stimuli defined in Table 7.1 and
the additional seven test samples (these were not used in the characterization
procedure) is, on average, just under 1.7 CIELAB units.
An additional function, rgb2xyz, has been provided to convert RGB DAC
values (in the range 0–255) to CIE XYZ values directly. The format of the
rgb2xyz function is

[xyz] = rgb2xyz(dacs, gogvals, A),

where dacs is a 361 matrix of RGB DAC values and the returned 361 matrix
xyz contains the predicted CIE values. The 362 matrix gogvals must contain the
gamma and gain terms of the GOG model for each of the three channels,
whereas the matrix A must contain the 363 matrix that transforms linearized
RGB values to XYZ values.