![]() ![]() It appears that the profile should have more digits than it shows. So, I measure red to have… divide by 100… and if I round it to 3 places, I get… and then I recall the first column of M (actually, the first row of M transpose). My pure red is supposed to match the XYZ coordinates of the red phosphor. How close are they to either the measured or ColorSync-computed values? Here are the differences from the measured values, rounded to. … and I arrange those nine values in a 3×3 matrix M (and I define the transpose MT):Īpply the nonlinear function to the measured RGBĪpply M to each vector (actually, apply 100 times M) (There’s nothing I can do about them, either.)įrom the phosphor coordinates, we will need (and, once again, there are two additional identical graphs, labelled for green and blue): Here are the absolute errors…įor the present, I’m OK with these measured and ColorSync numbers. The two sets of numbers are pretty close. Let’s compare those to the measured tristimuus values: That is, I open the ColorSync Utility and I ask it to calculate tristimulus values (perceptual to perceptual) for each of the measured RGB values. Here are the measured numbers, all in one long list (because I’m going to apply the nonlin function to each number): Recall my artists’ color wheel and recall the measured RGB… This means that the phosphors will not be affected by the nonlinear function. The function, however, does have one mandatory property: it maps 0 and 1 to 0 and 1. I’m still mulling over whether this is a problem for inverting the function. The values are not the same the function is not continuous at. … while the linear term… evaluated at the point. That is, the power term… evaluated at the point. I’m a little unhappy with this function: it is not continuous at the break point. So let’s just code up their function f(x) with those parameters: I think I know what the following does (and, yes, there are two additional identical graphs, presumably for green and blue)… Let me remark that the numbers listed for the “PCS Illuminant” are almost exactly what I measured for the white in the previous post. The profile is now dated 6/24/11 but its immediate predecessor was dated earlier this year – which was still newer than the one I used for the previous measurements.Īnyway, it appears that I need to work out a new relationship between RGB and XYZ on this monitor. And just to be safe, I should probably check the XYZ values for that purple disk whenever I start playing with color. I will probably have to make a habit of checking the profile after I install system updates. ![]() Let me say that I don’t particularly trust the new profile… I don’t know why it was changed, how often it was changed, when it will change next. The smallest change is more than 16%! My monitor profile seems to have changed! Recall the purple disk that figured so prominently in that post. Well, I showed us how to convert between RGB and XYZ, in a previous post about the nonlinearity of my monitor. Since CIELab is defined in terms of XYZ, but my monitor (“Color LCD” on a MacBook) is RGB, I’d like to be able to convert between RGB and CIELab. I set out to do something very simple: let Mathematica do the transformations between RGB and XYZ. abstract algebra adjoint and/or transpose algebraic topology attitude and/or transition matrices Bartholomew et al Basilevsky books Brereton calculus change of basis classical mechanics Cohen color ColorSync Utility control theory coordinate transformations correlation Davis differential geometry DigitalColor Meter dynamical systems eigenvector euler characteristic euler number Fourier games geometry graph theory group theory Harman ICC profiles Jolliffe latex linear algebra linear programming logic Malinowski manifolds mars math mathematics math models matrix exponential McMahon multicollinearity ODE ordinary differential equations OLS ordinary least squares regression orbital mechanics orthogonal PCA FA principal components factor analysis poker preprocessing probability pseudo-inverse puzzle QM quantum mechanics quaternions questions reciprocal basis dual basis Rip rotations Schiff schur similar similarity simplex simplices SN or NS decomposition surfaces SVD singular value decomposition SVD singular value decomposition linear algebra target testing time series topology triangulations trusses wavelets.
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