Page 109 - Computational Colour Science Using MATLAB

96 CHROMATIC-ADAPTATION TRANSFORMS AND COLOUR APPEARANCE
Step 13: Calculate the chroma of the sample (C),

n
C ¼ 2:44s 0:69 ðJ=100Þ 0:67n ð1:64 0:29 Þ. ð6:26Þ

Step 14: Calculate the colourfulness of the sample (M),

M ¼ CF 0:15 . ð6:27Þ
L

6.3.2 CMCCAM2000

Although CIECAM97s is widely used in the colour-management industry a
number of alternative models have been produced. Currently there is much focus
on the nature of the CAT that should be used (recall that CIECAM97s uses
CMCCAT97). Nayatani et al. (1999) have recently proposed an alternative CAT
and a further transform M SHARP has been developed based directly upon the
principle of chromatic sharpening (Finlayson and Su ¨ sstrunk, 2000). Luo and his
colleagues (Li et al., 2002) have developed CMCCAT2000 and this has been
adopted by the Colour Measurement Committee (CMC) of the Society of Dyers
and Colourists. It has been claimed that CMCCAT2000 gives a prediction to
almost all of the available data sets that is more accurate than any of the other
published transforms (Li et al., 2002). CMCCAT2000 is the CAT that forms the
basis of a colour-appearance model known as CMCCAM2000.

6.4 Implementations and examples

6.4.1 CATs

The function cmccat97 implements CMCCAT97 and the format for the function is

[xyzc] = cmccat97(xyz, xyzt, xyzr, la, f)

where xyz is a 361 matrix of the tristimulus values for the sample under the test
illuminant, xyzt and xyzr are 361 matrices whose entries hold the white points
of the adopted test and reference illuminants, respectively, and la and f are
parameters (both 161). The parameter la holds the luminance of the adapting
2
test field and this has a default value of 100 cd/m . The parameter f has a default
value of 1.0 and this corresponds to typical viewing conditions (a value of 0.9
should be used for dark or dim conditions).