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

Most software around us today are decent at accurately displaying colors. Processing of colors is another story unfortunately, and is often done badly.

To understand what the problem is, let’s start with an example of three ways of blending green and magenta:

• Perceptual blend – A smooth transition using a model designed to mimic human perception of color. The blending is done so that the perceived brightness and color varies smoothly and evenly.
• Linear blend – A model for blending color based on how light behaves physically. This type of blending can occur in many ways naturally, for example when colors are blended together by focus blur in a camera or when viewing a pattern of two colors at a distance.
• sRGB blend – This is how colors would normally be blended in computer software, using sRGB to represent the colors. 

Let’s look at some more examples of blending of colors, to see how these problems surface more practically. The examples use strong colors since then the differences are more pronounced. This is using the same three ways of blending colors as the first example.

Instead of making it as easy as possible to work with color, most software make it unnecessarily hard, by doing image processing with representations not designed for it. Approximating the physical behavior of light with linear RGB models is one easy thing to do, but more work is needed to create image representations tailored for image processing and human perception.

Also see:

https://www.pixelsham.com/2022/04/05/bjorn-ottosson-okhsv-and-okhsl-two-new-color-spaces-for-color-picking/

https://thomasmansencal.substack.com/p/the-apparent-simplicity-of-rgb-rendering

The primary goal of physically-based rendering (PBR) is to create a simulation that accurately reproduces the imaging process of electro-magnetic spectrum radiation incident to an observer. This simulation should be indistinguishable from reality for a similar observer.

Because a camera is not sensitive to incident light the same way than a human observer, the images it captures are transformed to be colorimetric. A project might require infrared imaging simulation, a portion of the electro-magnetic spectrum that is invisible to us. Radically different observers might image the same scene but the act of observing does not change the intrinsic properties of the objects being imaged. Consequently, the physical modelling of the virtual scene should be independent of the observer.

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.

• L: Lightness (the perceived brightness of the color)
• C: Chroma (the intensity or saturation of the color)
• H: Hue (the actual color, such as red, blue, green, etc.)

Also read: