Add support for ICtCp color space #216

devgru · 2024-01-01T12:20:41Z

Happy New Year! 🎄

This PR introduces support for ICtCp color space.

Coloraide supports this color space for three years so I thought it would be safe to reuse @facelessuser's implementation.

The code is slightly simplified to be aligned with the rest of color spaces implementations supported by Culori, e.g. matrix-vector multiplication replaced with basic multiplication.

I've also updated docs, tests and API exports.

The PR also includes small fix of tools/ranges.js that should theoretically increase its precision. Currently for loops for r, g, b never hit exact 1 as step is not a power of 2.

danburzo · 2024-01-03T22:22:44Z

Thanks for the contribution!

Before reviewing in detail I would like to get up to speed with (at least) CSS Color HDR Module.

In the meantime, could you make an editorial change to rename this color space from ictcp to itp, and the component names from i, ct, and cp to i, t, and p respectively? A similar simplified scheme is used for Jzazbz (jab).

devgru · 2024-01-04T07:29:57Z

@danburzo you're welcome!

I will apply proposed naming change on weekend.

If you want to run the formula for different colors, feel free to fork my notebook that uses minified version of the same code.

BTW I've noted that there's a slight difference between spec-defined ICtCp values for lime color and its representation in coloraide:

spec: color(ictcp 0.5393 -0.2643 -0.0625);

coloraide:
>>> Color('lime').convert('ictcp')
color(--ictcp 0.53976 -0.28125 -0.04948 / 1)

Proposed formula implementation is ported from coloriade and thus yields same numbers lime. I don't think this would be an issue but it might be a good idea to pinpoint the difference between spec and coloraide and mitigate it.

facelessuser · 2024-01-04T13:44:34Z

Because I was tagged here, I've been following along. In case there is some concern about lime and the CSS spec, I will redirect you to the issue where the ICtCp matrix was improved from what was originally used. These changes were made very recently on Color.js, the library used by the spec writers, so no doubt the spec has not been updated with recent changes: color-js/color.js#365.

Basically, we are now using the more accurate rational number matrix from the ICtCp paper. It was given in the paper to convert the RGB values to LMS, but was not used in the calculation from XYZ via the authors. But since we are going from XYZ, you have to apply the appropriate XYZ matrix to get the more accurate XYZ to LMS matrix. This gives you values very close to zero for T and P for achromatic values. This of course improves accuracy for non-achromatic values as well.

facelessuser · 2024-01-04T13:48:43Z

For the most accurate matrix, you can recalculate the matrix yourself with your own XYZ/Rec. 2020 matrix as shown here: https://github.com/facelessuser/coloraide/blob/main/tools/calc_ictcp_matrices.py (in case your XYZ/Rec. 2020 transform matrix is slightly different than Color.js).

devgru · 2024-01-06T19:38:53Z

@danburzo I've applied proposed renames, now naming is consistent with jab.

devgru · 2024-01-06T20:00:49Z

@facelessuser thanks so much for pointing out these nuances. I've experimented a bit with within mentioned notebook and I saw that with matrices copied from coloriade greyscale colors in culori yield ct and cp values under 1.5e-8. Should this level of precision be considered good enough?

facelessuser · 2024-01-06T20:58:15Z

I imagine you can get better than 1e-8.

I get somewhere around 1e-16 to 2e-16. You can see live results for ColorAide here.

Color.js gets close-ish to these results as well:

There are obviously a number of factors that can affect the accuracy. We are introducing chromatic adaptation when going from Lab (D50) to ICtCp (D65), so the accuracy of those matrices are important. Second, the matrix can be more or less accurate depending on how good the Rec. 2020 to RGB matrix is and how well it aligns with the actual white point being used. I have not evaluated where and how Culori has come up with its matrices and specific white points being used.

The most important thing is consistency. So, whatever white point Culori is using, it should be used to calculate the Rec.2020 RGB matrix, and then that matrix should be used to calculate the ICtCp matrix.

danburzo · 2024-01-08T10:35:35Z

Thanks @devgru for the updates, and thanks @facelessuser for the pointers!

Reading the Dolby paper, I’m on the fence in regards to what constitutes the canonical conversion to LMS. Is it defined by the intent, ie. the Hunt-Pointer-Estevez XYZ to LMS adjusted with the crosstalk matrix? Or is it the direct rec2020-to-LMS matrix from BT.2100 which, in the words of the authors, was defined like that “for convenience”?

In the PR on color.js Isaac mentions fixing the rough conversion in ICtCp by preferring the BT.2100 definition (LMS from Rec2020 primaries), which is probably the way to go here as well.

For the PQ function, I’ve made a separate PR (now merged in main) for the EOTF/EOTF^-1 functions, which can be used here as well. (I didn’t use the OETF term for the PQ inverse, as it seems reserved for HLG). I don’t think the extra computations in npow() are necessarily warranted here: we only care for it to be well-behaved in the immediate vicinity of its defined domain, for which a simple guard clause is sufficient. (The same issue that cropped up with Jzazbz’s PQ-inspired function).

The rest of the concerns are stylistic in nature, I’ll fix them post-merge.

danburzo · 2024-01-08T11:17:33Z

Here are my results for the XYZ from/to LMS matrices:

import numpy as np;

np.set_printoptions(precision=16, sign='-', floatmode='fixed');

A = np.array([
    [1.7166511879712683, -0.3556707837763925, -0.2533662813736599],
    [-0.6666843518324893, 1.6164812366349395, 0.0157685458139111],
    [0.0176398574453108, -0.0427706132578085, 0.9421031212354739]
]);
B = np.array([
    [1688, 2146, 262],
    [683, 2951, 262],
    [99, 309, 3688]
]);
C = np.matmul(B / 4096, A);

# C
array([[ 0.3592832590121217,  0.6976051147779501, -0.0358915932320289],
       [-0.1929421675348214,  1.1025652058879112,  0.0293737368853360],
       [ 0.0070797844607477,  0.0748396662186366,  0.8433265453898765]])

# np.linalg.inv(C)
array([[ 2.0718247352617034, -1.3199721753353908,  0.1341516007515605],
       [ 0.3638803308718196,  0.6772948457894078, -0.0081041392691667],
       [-0.0496850897213268, -0.0490241910692021,  1.1853732722208565]])

facelessuser · 2024-01-08T13:24:54Z

Here are my results for the XYZ from/to LMS matrices:

Just make sure to dump the entire matrix up to 64 bits:

np.set_printoptions(precision=16, sign='-', floatmode='fixed')

facelessuser · 2024-01-08T13:28:23Z

If you are still getting only 1e-8 accuracy for achromatics, I would evaluate accuracy of CAT matrices or any matrices where the CAT was folded in, or just in general how the XYZ to RGB matrices were derived. There is no reason you shouldn't be able to get better than 1e-8 unless some of your matrices aren't quite accurate or you somehow used clamped some inverses to 32 bit or something like that.

danburzo · 2024-01-08T13:38:53Z

Just make sure to dump the entire matrix up to 64 bits

Oh, so that’s what was going on! Updated the results, thanks :-)

facelessuser · 2024-01-08T15:28:38Z

One thing that may contribute to bad accuracy is the fact that the old CSS spec calculated the D50 <-> D65 matrices with a 32 bit inverse. If you incorporated that into your logic, it would also explain the poor accuracy. I think in November I informed them of the inaccuracy, and it has since been updated. The latest is:

function D65_to_D50(XYZ) {
	// Bradford chromatic adaptation from D65 to D50
	// The matrix below is the result of three operations:
	// - convert from XYZ to retinal cone domain
	// - scale components from one reference white to another
	// - convert back to XYZ
	// see https://github.com/LeaVerou/color.js/pull/354/files
	
	var M =  [
		[  1.0479297925449969,    0.022946870601609652,  -0.05019226628920524  ],
		[  0.02962780877005599,   0.9904344267538799,    -0.017073799063418826 ],
		[ -0.009243040646204504,  0.015055191490298152,   0.7518742814281371   ]
	];

	return multiplyMatrices(M, XYZ);
}

function D50_to_D65(XYZ) {
	// Bradford chromatic adaptation from D50 to D65
	// See https://github.com/LeaVerou/color.js/pull/360/files
	var M = [
		[  0.955473421488075,    -0.02309845494876471,   0.06325924320057072  ],
		[ -0.0283697093338637,    1.0099953980813041,    0.021041441191917323 ],
		[  0.012314014864481998, -0.020507649298898964,  1.330365926242124    ]
	];

	return multiplyMatrices(M, XYZ);
}

devgru · 2024-01-08T19:49:52Z

Wow, thanks for iterating on matrices.

I was a bit confused with

your own XYZ/Rec. 2020 matrix

as I am not 100% familiar with culori source code. Now, having values calculated by Dan I will add test case to verify monochrome colors precision and update matrices.

I'll also use new PQ implementation.

Most likely I'll do that closer to weekend. Next week I'll have a vacation, so will have more spare time on weekdays.

devgru · 2024-01-12T17:24:47Z

Here's what I found before updating matrices.

We're pretty good with black representation in itp, values for #000 and lab({ l:0, a:0, b: 0 }) are equal and have decent accuracy:

> itp('#000')
      { mode: 'itp', i: 7.309559025783966e-7, t: -2.117582368135751e-22, p: 1.3234889800848443e-23 }
> itp(lab({ l: 100, a: 0, b: 0 }))
      { mode: 'itp', i: 7.309559025783966e-7, t: -2.117582368135751e-22, p: 1.3234889800848443e-23 }

That's not the case for white:

> itp('#fff')
      { mode: 'itp', i: 0.5806888810416109, t: 1.1102230246251565e-16, p: 2.914335439641036e-16 }
> itp(lab({ l: 100, a: 0, b: 0 }))
      { mode: 'itp', i: 0.5806888835268884, t: -1.2671075300119128e-8, p: -9.53966114436433e-10 }

The reason is that lab conversion for non-black colors has limited accuracy, e.g.:

> rgb(lab({ l: 100, a: 0, b: 0 }))
      { mode: 'rgb', r: 0.9999999958609177, g: 1.0000000207019206, b: 0.9999999737698589 }

This reduces achievable accuracy in lab ↔ itp conversions, so for this PR I'll focus on rgb ↔ itp conversions as rgb is the default fallback color model in culori.

facelessuser · 2024-01-12T17:33:06Z

Here's what I found before updating matrices.

We're pretty good with black representation in itp, values for #000 and lab({ l:0, a:0, b: 0 }) are equal and have decent accuracy:
> itp('#000')
      { mode: 'itp', i: 7.309559025783966e-7, t: -2.117582368135751e-22, p: 1.3234889800848443e-23 }
> itp(lab({ l: 100, a: 0, b: 0 }))
      { mode: 'itp', i: 7.309559025783966e-7, t: -2.117582368135751e-22, p: 1.3234889800848443e-23 }
That's not the case for white:
> itp('#fff')
      { mode: 'itp', i: 0.5806888810416109, t: 1.1102230246251565e-16, p: 2.914335439641036e-16 }
> itp(lab({ l: 100, a: 0, b: 0 }))
      { mode: 'itp', i: 0.5806888835268884, t: -1.2671075300119128e-8, p: -9.53966114436433e-10 }
The reason is that lab conversion for non-black colors has limited accuracy, e.g.:
> rgb(lab({ l: 100, a: 0, b: 0 }))
      { mode: 'rgb', r: 0.9999999958609177, g: 1.0000000207019206, b: 0.9999999737698589 }
This reduces achievable accuracy in lab ↔ itp conversions, so for this PR I'll focus on rgb ↔ itp conversions as rgb is the default fallback color model in culori.

That's an indication that Coluri is likely using an inaccurate CAT matrix (conversion from Lab's D50 white point to D65 white point). I mentioned earlier that CSS updated their matrix because they were using an inverse of the CAT at only 32 bit precision. This is why you see inaccuracies after ~7 - 8 digits.

danburzo · 2024-01-12T17:36:36Z

This reduces achievable accuracy in lab ↔ itp conversions, so for this PR I'll focus on rgb ↔ itp conversions as rgb is the default fallback color model in culori.

Yes, that would be perfect! I will revise the chromatic adaptation matrices in a separate PR.

devgru · 2024-01-12T19:22:58Z

As for matrices, there was a typo in one of them, 262 is used twice. The second occurrence should be replaced with 462.

Recalculated values are:

C = [[ 0.3592832590121217  0.6976051147779502 -0.0358915932320289]
 [-0.1920808463704995  1.1004767970374323  0.0753748658519118]
 [ 0.0070797844607477  0.0748396662186366  0.8433265453898765]]
 
Cinv = [[ 2.0701522183894219 -1.3263473389671556  0.2066510476294051]
 [ 0.3647385209748074  0.6805660249472270 -0.0453045459220346]
 [-0.0497472075358120 -0.0492609666966138  1.1880659249923042]]

I've applied updated matrix and ran a test that verifies that p and t values are less then 10e-14 for each RGB monochrome color.

The test fails in 17 cases out of 255 for matrices in current PR and 27 cases out of 255 for updated matrices — actually, I'm a bit surprised by this result.

Here's the test that I used:

const LIMIT = 1e-14;
tape('greyscale itp accuracy', t => {
	for (let i = 0; i < 256; i++) {
		const c = i / 255;
		const color = itp(rgb({ r: c, g: c, b: c }));

		t.true(LIMIT > Math.max(Math.abs(color.p), Math.abs(color.t)), c * 255);
	}
	t.end();
});

danburzo · 2024-01-12T19:42:48Z

The typo notwithstanding (thank you for catching that) all other effects potentially stem from the matrix choices in various conversions. I’ve just pushed the (corrected) Python script to the repo so we have a reference, and I will incorporate updated matrices for itp operations when upgrading the precision of the other matrices.

facelessuser · 2024-01-12T19:44:10Z

The test fails in 17 cases out of 255 for matrices in current PR and 27 cases out of 255 for updated matrices — actually, I'm a bit surprised by this result.

I'm not surprised. The results are much better than what the HDR spec was showing. Results are better than 32 bit. ColorAide also has some cases that are slightly higher than 1e-14. These are still really good results compared to the previous results. Live example here.

>>> LIMIT = 1e-14
>>> count = 0
>>> for i in range(256):
...     c = i / 255
...     color = Color('srgb', [c] * 3).convert('ictcp')
...     if LIMIT < max(abs(color['cp']), abs(color['ct'])):
...         print(color.coords())
...         count += 1
... 
[0.3217444983163378, 9.43689570931383e-15, 1.211530875622202e-14]
[0.36221688204506475, 1.0658141036401503e-14, 1.3579415369946446e-14]
[0.3888049160915181, 1.1879386363489175e-14, -9.645062526431047e-16]
[0.3906142708446122, -1.1102230246251565e-14, -3.019112737590035e-14]
[0.40124034856142143, 1.2212453270876722e-14, -8.743006318923108e-16]
[0.40297423934753707, -1.199040866595169e-14, -1.5432100042289676e-14]
[0.419767351862685, 2.4646951146678475e-14, 3.156502836887398e-14]
[0.44024139094244685, -1.3211653993039363e-14, 1.124100812432971e-15]
[0.452158773392722, 2.6645352591003757e-14, 3.39797634474337e-14]
[0.46500508165983473, -1.3211653993039363e-14, -3.6241842860107454e-14]
[0.4732631623179539, -1.4210854715202004e-14, -1.8041124150158794e-14]
[0.47461687454901896, -1.3877787807814457e-14, -1.765254609153999e-14]
[0.486523099789503, -6.994405055138486e-15, -1.9081958235744878e-14]
[0.5077539929125083, -1.4988010832439613e-14, -1.9373391779708982e-14]
[0.517227703532751, -1.554312234475219e-14, 1.0685896612017132e-15]
[0.5183902459102809, 7.216449660063518e-15, 1.9942381079829374e-14]
[0.5218498815349994, -1.4765966227514582e-14, -4.0190073491430667e-14]
[0.5673242146934809, -1.609823385706477e-14, -4.3950953987348385e-14]
>>> print(f'Total colors above threshold = {count}')
Total colors above threshold = 18

devgru · 2024-01-13T16:58:46Z

I will incorporate updated matrices for itp operations when upgrading the precision of the other matrices.

@danburzo that sounds great!

BTW given current worse test results for updated matrices, would you like me to do any alterations to this PR or we're good to merge as is?

danburzo · 2024-01-13T19:48:14Z

I think we should, as they’re derived from the matrix we’re currently using.

devgru · 2024-01-14T05:57:24Z

Alright, I've updated matrices in the PR.

danburzo · 2024-01-14T10:22:27Z

Perfect, thank you!

danburzo · 2024-01-14T15:15:53Z

I’ve made some code style / docs changes here: 53b4479

The itp color space is due for culori@4.0.0 along with some major (breaking) changes, after I figure out if there are any other breaking changes to batch with this version bump.

devgru · 2024-01-14T17:03:10Z

@danburzo thanks so much!

BTW what do you think of the state of types package?

It's just slightly behind the current version, lacking maybe a couple of functions (I've noticedinGamut being absent) and new color models.

If you're planning to release v4 anytime soon, I think it might be a good idea to also update types for the package, I might invest some time into the update.

danburzo · 2024-01-15T11:34:33Z

BTW what do you think of the state of types package?

As you can read in #128, I have not been involved in developing the types for Culori, and I lack the time and skill to manage them. I’m happy, however, if the community is willing to maintain a types package.

danburzo · 2024-01-19T17:11:38Z

Released in culori@4.0.0. Thanks again @devgru @facelessuser!

devgru force-pushed the ictcp branch 2 times, most recently from 10a69a9 to fe3de46 Compare January 2, 2024 10:47

devgru force-pushed the ictcp branch 5 times, most recently from a9bcd67 to 88c4ec0 Compare January 6, 2024 19:38

devgru force-pushed the ictcp branch from 88c4ec0 to 30413ad Compare January 10, 2024 20:10

Use power of two as a step for ranges approximator

97a8246

Add support for ITP (ICtCp) color space

e723e0a

devgru force-pushed the ictcp branch from 30413ad to e723e0a Compare January 14, 2024 05:57

danburzo merged commit 28646c6 into Evercoder:main Jan 14, 2024

devgru deleted the ictcp branch January 14, 2024 16:57

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Add support for ICtCp color space #216

Add support for ICtCp color space #216

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Add support for ICtCp color space #216

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