Usability of Digital Cameras for Verifying Physically Based ...

(a) (b)
Figure 53: (a) Density plot of the L ∗ a ∗ b ∗ differences for using the camera’s color
space for test case 1 (8bit). Dark areas mean low L ∗ a ∗ b ∗ differences, while bright
areas mean high L ∗ a ∗ b ∗ differences; (b) a photograph of the test chart.
7.2 Using an ICC Profile
Instead of using the built-in color management we now generated an ICC profile
for our camera. We assumed that the built-in software is based on the average of
fluctuations due to the manufacturing process. Therefore, we supposed that actu-
ally measuring the behavior of our camera would give more accurate results. ICC
profiles for digital cameras can be generated without the use of a spectrophoto-
meter, as long as the software supports the test target that is used. Publicly avail-
able profiling software often performs worse than commercial software [FS02].
We used Gretag Macbeth Profile Maker 5.0.5 (around 2000,- Euros).
We created three ICC profiles for our camera based on the three test cases de-
scribed in section 7.1. For each test case, we used the profile together with the
lcms library to convert the average RGB values of the patches to L ∗ a ∗ b ∗ color
space. The histograms of the three test cases (see figure 55) show, that the res-
ult is considerably better than with only using the camera’s color space, as the
maximum values are much lower when an ICC profile is used. Median and mean
values – that can be found in table 10, together with the maximum and minimum
values and the standard deviations – are only slightly lower, though. Detailed
results for all 140 patches are listed in appendix E.2.
The density plot (see figure 56) for test case 1 shows that dark patches cause
fewer problems than bright patches. There is no clear tendency to a color as in the
previous approach, though. The scatter plots for L ∗ , a ∗ , and b ∗ (see figure 57(c))
show that the tendency to underestimation of L ∗ and to a shift of color for b ∗ is
also present in this approach.
73