Virtual Camera Primaries Rendering of Spectral Locus

[Troy_James_Sobotka (troy_s)]

This is based heavily on @Thomas_Mansencal’s excellent work, and is frankly just a minor tweak on his views of the data.

There has been quite a discussion about “data” with respect to cameras and their capturing. I thought it would be informative to plot the camera’s native response to the spectral locus with respect to how the values end up after processing via a naive set of three virtual camera primaries.

As we can see, the idea that the camera captures “data” is only relative to the constrained selection roughly around the solved set of swatches in question, and even then, it deviates. This seems like an unavoidable subject when determining a parametric or otherwise approach to gamut mapping, as the procedure would inevitably be mapping non-data.

Although the spectral responses are varied, the overall trends are perhaps striking.

I hope someone finds this animation useful. The sources are located here via a Colab.

[colorbycontrast (Fabián Matas González)]

Thanks Troy It’s really interesting

[daniele (Daniele Siragusano)]

Once you see such image you understand why the spectral locus is a fuzzy area and not a sharp boundry :-). Clearly important for IDT creation.

[Thomas_Mansencal]

@daniele

Once you see such image you understand why the spectral locus is a fuzzy area and not a sharp boundry :-). Clearly important for IDT creation.

This is more this one that gives me fuzziness

Cheers,

Thomas

[daniele (Daniele Siragusano)]

@Troy_James_Sobotka

It strikes me that it is more about the forced virtual camera primaries providing an unfit solve that results in non-data due to the dimensionality of the fit?

Can you elaborate on this? I think I cannot fully follow.

@Thomas_Mansencal

This is more this one that gives me fuzziness

Have you ever plotted all the spectral loci into one xy diagram? Should look interesting too.

[Thomas_Mansencal]

@daniele

Have you ever plotted all the spectral loci into one xy diagram? Should look interesting too.

Indeed:

Google Colaboratory

[daniele (Daniele Siragusano)]

pure fuzziness
Great!

[KevinJW (Kevin Wheatley)]

Interesting to see where the HVS fuzzy appears vs the camera variances
Kevin

[walter.arrighetti]

This diagram makes monitor calibration waaay less relevant than before…

@Thomas_Mansencal

@daniele

Have you ever plotted all the spectral loci into one xy diagram? Should look interesting too.

Indeed:

[daniele (Daniele Siragusano)]

@Troy_James_Sobotka

Here are the updated plots with the virtual camera primaries plotted in blue. As we can see, the primary cause for gamut mapping grief starts with the fitting of the spectral sensitivity region to a brute force 3x3.

absolutely, also the space virtual primaries span is not filled fully filled with achievable values (similar to XYZ being larger than the physically achievable colours). But I need to raise the this is an IDT subject buzzer here ;-). Out of Gamut Colours can have many causes and camera profiling is just one of it.

But this all cements the claim that the spectral locus should not be seen as a sharp border. We need to gradually lower or trust in data the further we move away from the centre.

[CedricLejeune]

@walter.arrighetti

This diagram makes monitor calibration waaay less relevant than before…

Well, we’ve had that experience, right? Stuff doesn’t look necessarily as the probe says it should… and on thop of that would be interesting to compare with the influence of perceived contrast vs perceived saturation…

[SeanCooper]

I’d tack on the usual misunderstanding of colorimetry, which was always a description of the stimuli not the sensation.

Human-inspired dimensional reduction ( 400 -> 3) is a better mental framework for it in my humble opinion.