COLOR

  • Björn Ottosson – OKlch color space

    https://abhisaha.com/blog/interactive-post-oklch-color-space

     

    Björn Ottosson proposed OKlch in 2020 to create a color space that can closely mimic how color is perceived by the human eye, predicting perceived lightness, chroma, and hue.

     

    The OK in OKLCH stands for Optimal Color.

    • LLightness (the perceived brightness of the color)
    • CChroma (the intensity or saturation of the color)
    • HHue (the actual color, such as red, blue, green, etc.)

     

     

    Also read:

     

     

     

    Read more: Björn Ottosson – OKlch color space
  • 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
  • Image rendering bit depth

    The terms 16-bit, 16-bit float, and 32-bit refer to different data formats used to store and represent image information, as bits per pixel.

     

    https://en.wikipedia.org/wiki/Color_depth

     

    In color technology, color depth also known as bit depth, is either the number of bits used to indicate the color of a single pixel, OR the number of bits used for each color component of a single pixel.

     

    When referring to a pixel, the concept can be defined as bits per pixel (bpp).

     

    When referring to a color component, the concept can be defined as bits per component, bits per channel, bits per color (all three abbreviated bpc), and also bits per pixel component, bits per color channel or bits per sample (bps). Modern standards tend to use bits per component, but historical lower-depth systems used bits per pixel more often.

     

    Color depth is only one aspect of color representation, expressing the precision with which the amount of each primary can be expressed; the other aspect is how broad a range of colors can be expressed (the gamut). The definition of both color precision and gamut is accomplished with a color encoding specification which assigns a digital code value to a location in a color space.

     

    (more…)

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    Read more: Image rendering bit depth
  • HDR and Color

    https://www.soundandvision.com/content/nits-and-bits-hdr-and-color

    In HD we often refer to the range of available colors as a color gamut. Such a color gamut is typically plotted on a two-dimensional diagram, called a CIE chart, as shown in at the top of this blog. Each color is characterized by its x/y coordinates.

    Good enough for government work, perhaps. But for HDR, with its higher luminance levels and wider color, the gamut becomes three-dimensional.

    For HDR the color gamut therefore becomes a characteristic we now call the color volume. It isn’t easy to show color volume on a two-dimensional medium like the printed page or a computer screen, but one method is shown below. As the luminance becomes higher, the picture eventually turns to white. As it becomes darker, it fades to black. The traditional color gamut shown on the CIE chart is simply a slice through this color volume at a selected luminance level, such as 50%.

    Three different color volumes—we still refer to them as color gamuts though their third dimension is important—are currently the most significant. The first is BT.709 (sometimes referred to as Rec.709), the color gamut used for pre-UHD/HDR formats, including standard HD.

    The largest is known as BT.2020; it encompasses (roughly) the range of colors visible to the human eye (though ET might find it insufficient!).

    Between these two is the color gamut used in digital cinema, known as DCI-P3.

    sRGB

    D65

     

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    Read more: HDR and Color

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