I believe it is MUCH more complex to describe what our eyes see given any specific

combined amount of color error than what has been said so far... Not that it can be described any better, just that our current methods are far from perfect.

I'm not a Physics or Math guy, but I'm pretty sure the generalities of the Navier Stokes problem applies here (even though we are dealing with color instead of fluid and the equations themselves are not directly applicable)...

A direct reference to some of the problems in logic are contained in this PDF about physics (

http://www.math.jussieu.fr/~boutet/P..._equations.pdf)

**From the above source:**
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The present problems are: how can one describe the phenomena with adequate equations, how can one compute them, and use and visualize

the results in spite of their complexity.

The question is the prediction, computation and representation of phenomena which contain no “mystery” in their generation but which are too complex

in their realization to be accessible to simple computations.

The equations involve some physical parameters and turn out to be relevant when these parameters have certain values. Therefore as an introduction it is natural to consider a “chain” of equations (and coderguy would add --- also to try to break everything down linearly is our human nature, but it doesn't always work out when testing the phenomena directly in the real world), so in hoping, as is often the case, that the next equation will become relevant when the structure of the phenomena becomes too complicated to be computed by the previous one.

It is an averaging process and the “turbulent model” starts to be efficient when the original

Navier Stokes are out of reach by direct numerical simulations.

In this averaging appears a hierarchy of moments which has been studied “per se” (cf. section

(3) and [24]). However this is not sufficient for the following reasons: (and goes on and on)....

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This problem is often cited in mathematics to show there can be limitations in the accuracy of measuring interpreted phenomena (like our eyesight or even the weather for instance), these limitations are in chained theories and equations as it relates to direct physical results (even a few variables could produce millions of varying results due to varying values within each of those variables), and the main limitation is that we cannot achieve certainty even if we attempt to solve all the resulting hierarchies of equations without actually being able to test each pattern against physical phenomena (in this case what our eyes interpret is the untested physical phenomena). That is why weather models get more accurate over time (they are using actual tested phenomena from historical data). In the color situation, many of this is untested because there are too many potential subjective results in each different chain in the end equation. So even if you think an equation or theory is perfectly sound mathematically in a linear fashion, the point is basically that with physical phenomena you potentially left it untested in thousands of other scenarios.

So to truly solve ALL of the color questions on what the EYE is seeing in any given COLOR ERROR situation, we would have to produce equations of nearly infinite magnitudes at varying degrees of percentage estimations in varying situations. However, the good news is we can still achieve some satisfying result without having to be this perfect, but we are really doing nothing more than using a system of predictions. You can be completely wrong some of the the time, or be ONLY partially wrong at varying levels most of the time.

Sorry for the RANT, and I have no opinion as to the exact amount this principle pollutes the calibration issue, but I am just saying the problem is there. I am not sure why I wrote all this, I guess I like throwing wrenches into things...

So the keywords in the above quotes are "hoping" and "averaging", and I guess the best we can really do in calibration is to average and hope...