Usability of Digital Cameras for Verifying Physically Based ...

7.4 Treating the Camera as a Black Box
The basic goal of color management is that of reproducing color as it is seen by
a human observer, like for example taking a photograph and displaying it on a
screen in a way that the colors match the original scene. Our problem is different
to that of color management, though. As stated in section 1, we primarily want
to verify the light transport simulation but not necessarily the subsequent steps of
image synthesis (like e.g. the tone mapping step). First, we have to prove that our
physically based rendering algorithm is implemented correctly. Only after that,
we can verify the tone mapping and gamut mapping algorithms. Otherwise we
would analyze two different steps in the rendering pipeline at once and – if errors
would occur – we could not say whether the tone mapping step is the cause of
error or the rendering algorithm itself is not performing correctly. One possible
solution to this problem is not to convert to XYZ or L ∗ a ∗ b ∗ color space at all, but
to do the comparison in RGB space.
After light hits the CCD, various processing steps are done in the camera.
Some of them can be simulated – like e.g. the interaction of light and CCD ele-
ments – but others, like e.g. color correction, cannot. Manufacturers usually do
not publish details about the internal processes of their cameras. We do not have
to care about steps like color correction if we use the RAW file format. But we
still have to know the camera’s spectral sensitivities and probably some details
about the behaviour of the CCD element itself to be able to convert a rendering to
a RAW file that is identical to a RAW photo of the same scene.
If we lack information about the camera’s spectral sensitivities, the following
method can be used. As we do not know exactly what is happening inside the
camera, we treat it as a black box. The input data of our black box is light, i.e.
light spectra. The output data is of type RGB, i.e. the RGB values of the final
image. What we want to find now is a mapping that simulates what happens
inside the black box for one pixel.
As described in section 5, the result of the light transport simulation consists
of one spectrum per pixel. If we apply the mapping to each of the spectra we get
an RGB value for each pixel. This procedure is illustrated by the dotted line in
figure 26. For verification purposes, this RGB value is now directly comparable
to the RAW RGB value of the correspondent pixel of an image produced by a
camera. A difference between these two values can be caused either by an error
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