"RGB to XYZ: The Science and History of Color" by John Austin
40,892
Published 2019-09-15
Many of us are familiar with the RGB or perhaps even the sRGB color space. Generally, though, we stare at our color pickers, fiddle with the values, and pick something that looks right. However, the modern sRGB specification follows a long lineage. It extends all the way back to the turn of the century and the CIE 1931 XYZ color space -- one of the earliest specified color spaces and one which is still in use today.
As display technology advanced, so did our color. New color spaces emerged: CIELUV, CIELAB, YUV, HSV, HSL, RGB, sRGB. Each was molded for a specific use case, whether it was print, perceptual uniformity, compression, or LED display. By the time we work our way back to the present day, we're left with a lot of color options and a lot of questions.
In reality, though, the math and science isn't all that complex. We'll start with color spaces like RGB that we know and love, and see how these naturally evolve mathematically from earlier color spaces such as CIE XYZ. More importantly, we'll see how to apply this knowledge to the decisions we make about color in our day to day work.
John Austin
A Stranger Gravity
@kleptine
John Austin is a developer and designer currently living in San Francisco, California. He has been making games for nearly 13 years and has worked at Google, Microsoft, Funomena, and others. He founded and currently leads the studio, A Stranger Gravity, seeking to build thoughtful, accessible experiences that seek to enrich the lives of people across the world.
All Comments (21)
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Speaker here! Thanks for watching! A few addendums: - Missed a citation: the painting at 29:53 is by the wonderful Kazuo Oga - The question at 36:41 is: "I have two monitors, why does an image look different when I drag it between them?" - The slide at 28:34 is mislabeled: The bar labeled "Linear sRGB" is actually "Non-Linear sRGB". When using Linear sRGB, you actually have the opposite effect -- the middle colors look way too bright. Happy to answer questions if you have them, too.
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Really nice talk, I spend like a week read wiki and read three books about color but still feel confused about CIE XYZ, CIE RBG, CIE LAB until I reach this video. Really helpful.
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Everybody interested (fascinated) by "color" as a concept should read "Catching the Light: The Entwined History of Light and Mind" by Arthur Zajonc
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Thanks a lot to the Speaker, John Austin! Now I actually understand, what this XYZ color space is and why it really can represent all colors in the visual spectrum... especially, how to interpret these 2D coordinate mappings, while actually having 3 color components. And that doing math in sRGB is a "bad" thing - and which colors paces to use for those calculations instead... That video just gave me a lot of answers in so short time!
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This talk was so much more interesting than I expected. Thanks a lot for this.
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It's basically a 30 minute presentation of the history of color and somehow it's better explained than in my multimedia classes
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That was awesome! Thanks so much; I've tried to understand computer color before by Wikipedia-surfing -- this was so nice to get a big picture explanation!
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Fascinating talk, thank you!
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Wow, such a great talk. Very interesting. Thank you very much. 👍🙏
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amazing talk!
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You did not mention that human vision also involves rods, which have a spectral sensitivity between the blue and green cones. In general, your considerations are a good approximation of color vision in bright light conditions, i.e. phototopic vision, but there is also mesopic and scotopic vision, where color information is very limited.
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This is so so interesting
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I'm making app for measuring colors, this video really helps, thanks!
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That was insanely interessting.
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I was wondering why we should pass from RGB to XYZ color space or vice versa ( I got the answer now, thank you)
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whoah dude , thank u
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Dear Sir , could you please tell me the precise value of the angle between the line of purples and the horizontal ? I tried to measure it on the screen of my laptop , but it is not very accurate . BTW is there also a precise equation for the curve depicting the spectral locus ?
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This was a nice deeper dive into color and I'm happy to have watched it as an addition to a video of what went into picking new default colormaps for Python's Matplotlib: https://www.youtube.com/watch?v=xAoljeRJ3lU Spoiler: they use (a version of) CIE-LAB to make them perceptually uniform, exactly as intended
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So I am often visualizing data with Matplotlib/Python and ImageJ. They both have colormaps such as Viridis and Inferno. As far as a I understand these colormaps are not interpolating between RGB values in the sRGB color space but they are rather LUTs that have been crafted to mimick the output one would get when interpolating in CIELAB? Is this correct? So when using Viridis there should be no advantage of changing to a CIELAB workflow? Another question: I am also working with Xray fluorescence measurements. In these measurements you spatially resolve the distribution of chemical elements. Often we visualize this type of data by assigning RGB to the three most prominent elements in the measurement, thus creating a color picture representing the result of the measurement. Use Google image search for "XRF RGB" to see some examples of what I mean. If I understood the talk correctly, CIELAB should yield a huge advantage in this case?
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That version of the Union Jack hadn't been in use for 130 years in 1931
