COLOR

  • No one could see the colour blue until modern times

    https://www.businessinsider.com/what-is-blue-and-how-do-we-see-color-2015-2

     

    The way that humans see the world… until we have a way to describe something, even something so fundamental as a colour, we may not even notice that something it’s there.

     

    Ancient languages didn’t have a word for blue — not Greek, not Chinese, not Japanese, not Hebrew, not Icelandic cultures. And without a word for the colour, there’s evidence that they may not have seen it at all.

    https://www.wnycstudios.org/story/211119-colors

     

    Every language first had a word for black and for white, or dark and light. The next word for a colour to come into existence — in every language studied around the world — was red, the colour of blood and wine.

    After red, historically, yellow appears, and later, green (though in a couple of languages, yellow and green switch places). The last of these colours to appear in every language is blue.

     

    The only ancient culture to develop a word for blue was the Egyptians — and as it happens, they were also the only culture that had a way to produce a blue dye.

    https://mymodernmet.com/shades-of-blue-color-history/

     

    Considered to be the first ever synthetically produced color pigment, Egyptian blue (also known as cuprorivaite) was created around 2,200 B.C. It was made from ground limestone mixed with sand and a copper-containing mineral, such as azurite or malachite, which was then heated between 1470 and 1650°F. The result was an opaque blue glass which then had to be crushed and combined with thickening agents such as egg whites to create a long-lasting paint or glaze.

     

     

    If you think about it, blue doesn’t appear much in nature — there aren’t animals with blue pigments (except for one butterfly, Obrina Olivewing, all animals generate blue through light scattering), blue eyes are rare (also blue through light scattering), and blue flowers are mostly human creations. There is, of course, the sky, but is that really blue?

     

     

    So before we had a word for it, did people not naturally see blue? Do you really see something if you don’t have a word for it?

     

    A researcher named Jules Davidoff traveled to Namibia to investigate this, where he conducted an experiment with the Himba tribe, who speak a language that has no word for blue or distinction between blue and green. When shown a circle with 11 green squares and one blue, they couldn’t pick out which one was different from the others.

     

    When looking at a circle of green squares with only one slightly different shade, they could immediately spot the different one. Can you?

     

    Davidoff says that without a word for a colour, without a way of identifying it as different, it’s much harder for us to notice what’s unique about it — even though our eyes are physically seeing the blocks it in the same way.

     

    Further research brought to wider discussions about color perception in humans. Everything that we make is based on the fact that humans are trichromatic. The television only has 3 colors. Our color printers have 3 different colors. But some people, and in specific some women seemed to be more sensible to color differences… mainly because they’re just more aware or – because of the job that they do.

    Eventually this brought to the discovery of a small percentage of the population, referred to as tetrachromats, which developed an extra cone sensitivity to yellow, likely due to gene modifications.

    The interesting detail about these is that even between tetrachromats, only the ones that had a reason to develop, label and work with extra color sensitivity actually developed the ability to use their native skills.

     

    So before blue became a common concept, maybe humans saw it. But it seems they didn’t know they were seeing it.

    If you see something yet can’t see it, does it exist? Did colours come into existence over time? Not technically, but our ability to notice them… may have…

     

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  • Weta Digital – Manuka Raytracer and Gazebo GPU renderers – pipeline

    https://jo.dreggn.org/home/2018_manuka.pdf

     

    http://www.fxguide.com/featured/manuka-weta-digitals-new-renderer/

     

    The Manuka rendering architecture has been designed in the spirit of the classic reyes rendering architecture. In its core, reyes is based on stochastic rasterisation of micropolygons, facilitating depth of field, motion blur, high geometric complexity,and programmable shading.

     

    This is commonly achieved with Monte Carlo path tracing, using a paradigm often called shade-on-hit, in which the renderer alternates tracing rays with running shaders on the various ray hits. The shaders take the role of generating the inputs of the local material structure which is then used bypath sampling logic to evaluate contributions and to inform what further rays to cast through the scene.

     

    Over the years, however, the expectations have risen substantially when it comes to image quality. Computing pictures which are indistinguishable from real footage requires accurate simulation of light transport, which is most often performed using some variant of Monte Carlo path tracing. Unfortunately this paradigm requires random memory accesses to the whole scene and does not lend itself well to a rasterisation approach at all.

     

    Manuka is both a uni-directional and bidirectional path tracer and encompasses multiple importance sampling (MIS). Interestingly, and importantly for production character skin work, it is the first major production renderer to incorporate spectral MIS in the form of a new ‘Hero Spectral Sampling’ technique, which was recently published at Eurographics Symposium on Rendering 2014.

     

    Manuka propose a shade-before-hit paradigm in-stead and minimise I/O strain (and some memory costs) on the system, leveraging locality of reference by running pattern generation shaders before we execute light transport simulation by path sampling, “compressing” any bvh structure as needed, and as such also limiting duplication of source data.
    The difference with reyes is that instead of baking colors into the geometry like in Reyes, manuka bakes surface closures. This means that light transport is still calculated with path tracing, but all texture lookups etc. are done up-front and baked into the geometry.

     

    The main drawback with this method is that geometry has to be tessellated to its highest, stable topology before shading can be evaluated properly. As such, the high cost to first pixel. Even a basic 4 vertices square becomes a much more complex model with this approach.

     

     

    Manuka use the RenderMan Shading Language (rsl) for programmable shading [Pixar Animation Studios 2015], but we do not invoke rsl shaders when intersecting a ray with a surface (often called shade-on-hit). Instead, we pre-tessellate and pre-shade all the input geometry in the front end of the renderer.
    This way, we can efficiently order shading computations to sup-port near-optimal texture locality, vectorisation, and parallelism. This system avoids repeated evaluation of shaders at the same surface point, and presents a minimal amount of memory to be accessed during light transport time. An added benefit is that the acceleration structure for ray tracing (abounding volume hierarchy, bvh) is built once on the final tessellated geometry, which allows us to ray trace more efficiently than multi-level bvhs and avoids costly caching of on-demand tessellated micropolygons and the associated scheduling issues.

     

    For the shading reasons above, in terms of AOVs, the studio approach is to succeed at combining complex shading with ray paths in the render rather than pass a multi-pass render to compositing.

     

    For the Spectral Rendering component. The light transport stage is fully spectral, using a continuously sampled wavelength which is traced with each path and used to apply the spectral camera sensitivity of the sensor. This allows for faithfully support any degree of observer metamerism as the camera footage they are intended to match as well as complex materials which require wavelength dependent phenomena such as diffraction, dispersion, interference, iridescence, or chromatic extinction and Rayleigh scattering in participating media.

     

    As opposed to the original reyes paper, we use bilinear interpolation of these bsdf inputs later when evaluating bsdfs per pathv ertex during light transport4. This improves temporal stability of geometry which moves very slowly with respect to the pixel raster

     

    In terms of the pipeline, everything rendered at Weta was already completely interwoven with their deep data pipeline. Manuka very much was written with deep data in mind. Here, Manuka not so much extends the deep capabilities, rather it fully matches the already extremely complex and powerful setup Weta Digital already enjoy with RenderMan. For example, an ape in a scene can be selected, its ID is available and a NUKE artist can then paint in 3D say a hand and part of the way up the neutral posed ape.

     

    We called our system Manuka, as a respectful nod to reyes: we had heard a story froma former ILM employee about how reyes got its name from how fond the early Pixar people were of their lunches at Point Reyes, and decided to name our system after our surrounding natural environment, too. Manuka is a kind of tea tree very common in New Zealand which has very many very small leaves, in analogy to micropolygons ina tree structure for ray tracing. It also happens to be the case that Weta Digital’s main site is on Manuka Street.

     

     

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  • Björn Ottosson – OKHSV and OKHSL – Two new color spaces for color picking

    https://bottosson.github.io/misc/colorpicker

     

    https://bottosson.github.io/posts/colorpicker/

     

    https://www.smashingmagazine.com/2024/10/interview-bjorn-ottosson-creator-oklab-color-space/

     

    One problem with sRGB is that in a gradient between blue and white, it becomes a bit purple in the middle of the transition. That’s because sRGB really isn’t created to mimic how the eye sees colors; rather, it is based on how CRT monitors work. That means it works with certain frequencies of red, green, and blue, and also the non-linear coding called gamma. It’s a miracle it works as well as it does, but it’s not connected to color perception. When using those tools, you sometimes get surprising results, like purple in the gradient.

     

     

    There were also attempts to create simple models matching human perception based on XYZ, but as it turned out, it’s not possible to model all color vision that way. Perception of color is incredibly complex and depends, among other things, on whether it is dark or light in the room and the background color it is against. When you look at a photograph, it also depends on what you think the color of the light source is. The dress is a typical example of color vision being very context-dependent. It is almost impossible to model this perfectly.

     

    I based Oklab on two other color spaces, CIECAM16 and IPT. I used the lightness and saturation prediction from CIECAM16, which is a color appearance model, as a target. I actually wanted to use the datasets used to create CIECAM16, but I couldn’t find them.

     

    IPT was designed to have better hue uniformity. In experiments, they asked people to match light and dark colors, saturated and unsaturated colors, which resulted in a dataset for which colors, subjectively, have the same hue. IPT has a few other issues but is the basis for hue in Oklab.

     

    In the Munsell color system, colors are described with three parameters, designed to match the perceived appearance of colors: Hue, Chroma and Value. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. Modern color spaces and models, such as CIELAB, Cam16 and Björn Ottosson own Oklab, are very similar in their construction.

     

     

    By far the most used color spaces today for color picking are HSL and HSV, two representations introduced in the classic 1978 paper “Color Spaces for Computer Graphics”. HSL and HSV designed to roughly correlate with perceptual color properties while being very simple and cheap to compute.

     

    Today HSL and HSV are most commonly used together with the sRGB color space.

     

     

    One of the main advantages of HSL and HSV over the different Lab color spaces is that they map the sRGB gamut to a cylinder. This makes them easy to use since all parameters can be changed independently, without the risk of creating colors outside of the target gamut.

     

     

    The main drawback on the other hand is that their properties don’t match human perception particularly well.
    Reconciling these conflicting goals perfectly isn’t possible, but given that HSV and HSL don’t use anything derived from experiments relating to human perception, creating something that makes a better tradeoff does not seem unreasonable.

     

     

    With this new lightness estimate, we are ready to look into the construction of Okhsv and Okhsl.

     

     

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    Read more: Björn Ottosson – OKHSV and OKHSL – Two new color spaces for color picking

LIGHTING