Is there a way to check if an XYZ triplet is a valid color? - image-processing

Question

The XYZ color space encompasses all possible colors, not just those which can be generated by a particular device like a monitor. Not all XYZ triplets represent a color that is physically possible. Is there a way, given an XYZ triplet, to determine if it represents a real color?
I wanted to generate a CIE 1931 chromaticity diagram (seen bellow) for myself, but wasn't sure how to go about it. It's easy to, for example, take all combinations of sRGB triplets and then transform them into the xy coordinates of the chromaticity diagram and then plot them. You cannot use this same approach in the XYZ color space though since not all combinations are valid colors. So far the best I have come up with is a stochastic approach, where I generate a random spectral distribution by summing a random number of random Gaussians, then converting it to XYZ using the standard observer functions.

Answer 1

Having thought about it a little more I felt the obvious solution is to generate a list of xy points around the edge of spectral locus, corresponding to pure monochromatic colors. It seems to me that this can be done by directly inputting the visible frequencies (~380-780nm) into the CIE XYZ standard observer color matching functions. Treating these points like a convex polygon you could determine if a point is within the spectral locus using one algorithm or another. In my case, since what I really wanted to do is simply generate the chromaticity diagram, I simply input these points into a graphics library's polygon drawing routine and then for each pixel of the polygon I can transform it into sRGB.
I believe this solution is similar to the one used by the library that Kel linked in a comment. I'm not entirely sure, as I am not familiar with Python.
function RGBfromXYZ(X, Y, Z) {
const R = 3.2404542 * X - 1.5371385 * Y - 0.4985314 * Z
const G = -0.969266 * X + 1.8760108 * Y + 0.0415560 * Z
const B = 0.0556434 * X - 0.2040259 * Y + 1.0572252 * Z
return [R, G, B]
}
function XYZfromYxy(Y, x, y) {
const X = Y / y * x
const Z = Y / y * (1 - x - y)
return [X, Y, Z]
}
function srgb_from_linear(x) {
if (x <= 0.0031308) {
return x * 12.92
} else {
return 1.055 * Math.pow(x, 1/2.4) - 0.055
}
}
// Analytic Approximations to the CIE XYZ Color Matching Functions
// from Sloan http://jcgt.org/published/0002/02/01/paper.pdf
function xFit_1931(x) {
const t1 = (x - 442) * (x < 442 ? 0.0624 : 0.0374)
const t2 = (x -599.8) * (x < 599.8 ? 0.0264 : 0.0323)
const t3 = (x - 501.1) * (x < 501.1 ? 0.0490 : 0.0382)
return 0.362 * Math.exp(-0.5 * t1 * t1) + 1.056 * Math.exp(-0.5 * t2 * t2) - 0.065 * Math.exp(-0.5 * t3 * t3)
}
function yFit_1931(x) {
const t1 = (x - 568.8) * (x < 568.8 ? 0.0213 : 0.0247)
const t2 = (x - 530.9) * (x < 530.9 ? 0.0613 : 0.0322)
return 0.821 * Math.exp(-0.5 * t1 * t1) + 0.286 * Math.exp(-0.5 * t2 * t2)
}
function zFit_1931(x) {
const t1 = (x - 437) * (x < 437 ? 0.0845 : 0.0278)
const t2 = (x - 459) * (x < 459 ? 0.0385 : 0.0725)
return 1.217 * Math.exp(-0.5 * t1 * t1) + 0.681 * Math.exp(-0.5 * t2 * t2)
}
const canvas = document.createElement("canvas")
document.body.append(canvas)
canvas.width = canvas.height = 512
const ctx = canvas.getContext("2d")
const locus_points = []
for (let i = 440; i < 650; ++i) {
const [X, Y, Z] = [xFit_1931(i), yFit_1931(i), zFit_1931(i)]
const x = (X / (X + Y + Z)) * canvas.width
const y = (Y / (X + Y + Z)) * canvas.height
locus_points.push([x, y])
}
ctx.beginPath()
ctx.moveTo(...locus_points[0])
locus_points.slice(1).forEach(point => ctx.lineTo(...point))
ctx.closePath()
ctx.fill()
const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height)
for (let y = 0; y < canvas.height; ++y) {
for (let x = 0; x < canvas.width; ++x) {
const alpha = imageData.data[(y * canvas.width + x) * 4 + 3]
if (alpha > 0) {
const [X, Y, Z] = XYZfromYxy(1, x / canvas.width, y / canvas.height)
const [R, G, B] = RGBfromXYZ(X, Y, Z)
const r = Math.round(srgb_from_linear(R / Math.sqrt(R**2 + G**2 + B**2)) * 255)
const g = Math.round(srgb_from_linear(G / Math.sqrt(R**2 + G**2 + B**2)) * 255)
const b = Math.round(srgb_from_linear(B / Math.sqrt(R**2 + G**2 + B**2)) * 255)
imageData.data[(y * canvas.width + x) * 4 + 0] = r
imageData.data[(y * canvas.width + x) * 4 + 1] = g
imageData.data[(y * canvas.width + x) * 4 + 2] = b
}
}
}
ctx.putImageData(imageData, 0, 0)