Color Appearance Simulation
The grand question: what do colors look like to animals?
This is a
widely asked question by both scientists and the general public.Although it is a very interesting
and intriguing question, it is impossible to answer accurately and convincingly.
Let's analyze this
question. Suppose someone says: "The green leaves of a tree look yellow to
a dog." He or she must know the definition of "yellow" for dogs. There
is no definition of any color for any animal. Colors are not physical properties
of objects. They are senses or perceptions associated with optical properties
of objects. The association varies among animals. The sense of color is unique.
It is different from most other senses such as pain. On one hand it is like
the olfactory sense - what smells foul to us may be fragrant to some animals.
On the other hand it is different because we can have the same definition of foul
and fragrant for all animals and human beings because they are associated with certain
types of behavior, while colors are not associated with the same behavior for all
creatures. Anyone can tell what stinks to animals if that smell drives the
animal away, but no one can tell what looks red, yellow, green and so on by just
observing some behaviors of an animal. If we discuss this further, we will
unavoidably run into psychological, semantic, or even philosophic issues.
The ideal method
to visualize the color world of an animal is to create the animal's version of a
CIE chromaticity diagram. The CIE chromaticity diagram was created by doing
extensive color matching psychophysical experiments. If we could do the experiments
to get the animal's version of CIE chromaticity diagram., then we could compare
it with human's CIE chromaticity diagram to assign colors (actually names)
to the animal's CIE color chart. Since animals can not speak to us, anyone
can imagine how difficult the experiments would be.
There is no published
method to visualize any animal's color world. Many assumptions must be made
to do this and many of the assumptions are unfortunately debatable.
In the author's
dissertation done five years ago, the following
assumption is made:
The colors we would see if we replaced our visual pigments
with that of an animal are equivalent to the colors seen by that specific animal.
In other words,
the difference in color perception among animals is caused by the difference in
their visual pigment composition. Color depends only on the outputs of cones, or
put it in another way, the outputs of cones and colors have a one-to-one mapping
relationship universal to all animals. If we figure out this relationship
in humans, then we can apply this to other animals. This deals with only the
animals that are also trichromats. Simulating the color perception of animals
with more than three types of cones are probably beyond our capability. Similarly
we can assume what dichromatic animals see is equivalent to dichromatic humans would
see if their photopigments are replaced with the two of the dichromatic animal.
A simpler question: what do colors look like to human dichromats?
In other words,
how can we trichromats see the world like dichromats do?
Many people have
investigated this. The latest work (Brettel et al, 1997) on this has drawn
quite some interest. It presents an algorithm to transform CRT colors for
trichromats so that the output colors simulate the appearance to dichromats. Same
as other similar work, the algorithm is largely based on the data gathered from
unilateral dichromats. This is the strongest part of the foundation of the
algorithm. Although this is probably the best work in this category, I have
many questions regarding the paper and have been seeking answers from different
sources with little success:
The algorithm depends
on the spectral power distributions of the CRT used by the study (Hitachi Model
CM2086A3SG). Theoretically, even if all CRT's comply with the international standard
(i.e. produces the same RGB->CIEXYZ matrix), their RGB phosphors can have different
spectral characteristics (e.g. different shapes of spectral radiance). This
means the algorithm cannot be used on any other models of CRT without checking their
RGB spectral power distributions. However this algorithm has been probably implemented
in many places (e.g. www.vischeck.com) without
restricting the selection of the computer monitors. How good is the simulation
if this algorithm is applied to popular computer monitors?
According to the
algorithm and Fig. 3 in the paper, there are colors that can be produced by the
CRT (i.e. the colors in the parallelepiped) that are projected beyond the wings
along a LMS axis. This is briefly addressed in the discussion section of the
paper and it is implied that the trichromats can see all the colors that dichromats
can see. Therefore another question follows logically: How would those
colors be mapped or simulated?
The key part of
the algorithm is the two wings in the LMS 3-D space for each type of dichromat.
Although the concept of the wings is intuitive to certain degree, the paper
does not give the rationale behind the wings clearly. Is there a theoretical
or experimental foundation for the wings?
(one protanope and one deuteranope) were used to verify the simulation. The
description of the procedure and the results is very sketchy. Since dichromats constitute
one to two percent of the population, it is logical to ask: Are there any more
data using dichromats verifying the simulation? Obviously, the best validation
of the algorithm is to use unilateral dichromats to check the simulation however
this kind of subjects are hard to find.
Brettel, H. et
al. (1997) Computerized simulation of color appearance for dichromats. J. Opt. Soc.
Am. A 14, 2647-2655.
(originally written on 3/2/2002)