for lot of image processing linearizing srgb is only half-way measure, and you might want to reach for a better colorspace altogether. cielab is obvious example, or maybe oklab/jzazbz/xyb/something else.
Exactly this, there's rarely a single "correct" colorspace, you make a choice based on expressive goals and constraints. For example, for gradients you almost never want linear, something like Oklab is indeed much better.
The gradient examples between high-chroma colors of similar luminance are highly misleading in my opinion. In that particular case, linear just happens to do well (and device RGB of course poorly), but in other cases linear is not great. For example, blue to white is especially bad, with hue shifts as well as lightness non-uniformity.
You can experiment with this in the interactive tester in my Oklab review[1].
> On which image does the gradation appear more even? It’s the second one!
I felt the first one looked more even. On the first I could tell the difference between every two adjacent bars. On the second one I couldn't tell any difference between the first 4-5 bars.
> Pixels with RGB values of (128, 128, 128) emit about half as much light as pixels with RGB values of (255, 255, 255).
Technically, this is not always incorrect, if your working color space is linear and 0 is no light. The problem only comes if you hand that same data to routines or surfaces expecting sRGB or another nonlinear color space (or one where 0 is not no light).
Oh, I was just listing the constraints. I'm not directly aware of a color space where value 0 is not no light. It would however mean that even if linear, doubling a value relative to 0 wouldn't necessarily double the amount of light.
The gradient examples between high-chroma colors of similar luminance are highly misleading in my opinion. In that particular case, linear just happens to do well (and device RGB of course poorly), but in other cases linear is not great. For example, blue to white is especially bad, with hue shifts as well as lightness non-uniformity.
You can experiment with this in the interactive tester in my Oklab review[1].
[1]: https://raphlinus.github.io/color/2021/01/18/oklab-critique....
I felt the first one looked more even. On the first I could tell the difference between every two adjacent bars. On the second one I couldn't tell any difference between the first 4-5 bars.
Technically, this is not always incorrect, if your working color space is linear and 0 is no light. The problem only comes if you hand that same data to routines or surfaces expecting sRGB or another nonlinear color space (or one where 0 is not no light).
Oh, interesting. What's an example of this? Some sort of log space?
The bevel of a black iPhone is darker than its screen, even when powered off. Similarly, switched off CRT displays aren’t truly black.
The most common 8-bit YUV format (e.g. in MPEG-2) uses a 16-235 range for valid luma values, so black is at 16 and white is at 235.
The reason for leaving this “headroom” and “footroom” had to do both with digitizing analog signals and avoiding clipping during processing.