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Technically, image resizing does in general care about the color channel ordering, because color spaces are in general not linear. https://www.alanzucconi.com/2

Technically, image resizing does in general care about the color channel ordering, because color spaces are in general not linear. https://www.alanzucconi.com/2016/01/06/colour-interpolation/ https://www.alanzucconi.com/2016/01/06/colour-interpolation/

quietbritishjim 2y ago
That article fails to touch on the fundamental issue with its title "The Secrets of Colour Interpolation": RGB values are a nonlinear function of the light emitted (because we are better at distinguishing dark colours, so it's better to allow representing more of those), so to interpolate properly you need to invert that function, interpolate, then reapply. The difference that makes to colour gradients is really striking.
- topherclay 2y ago
  Do you mean converting the RGB value to LAB values in the CIELAB color space and doing the interpellation there? Is there a better way to do it?
  - CarVac 2y ago
    No, you just need to linearize the brightness.
    - drjasonharrison 2y ago
      Typically this is done by using a look-up table to convert the 8-bit gamma encoded intensities to 10-bit (or more) linear intensities. You can use the same look-up table for R, G, and B. Alpha should be linear.
yosefk 2y ago
While this is a valid point, cv2.resize doesn't actually implement color conversion in a way addressing this issue; at least in my testing I get identical results whether I interpret the data as RGB or BGR. So if you want to use cv2.resize, AFAIK you can count on it not caring which channel is which. And if you need fast resizing, you're quite likely to settle for the straightforward interpolation implemented by cv2.resize.
planede 2y ago
Even if the RGB components correspond to sRGB, to linearize you apply the same non-linear function to each component value, independently. So even if you do the interpolation in a linear colorspace, the order of the sRGB components does not matter.

quietbritishjim 2y ago

That article fails to touch on the fundamental issue with its title "The Secrets of Colour Interpolation": RGB values are a nonlinear function of the light emitted (because we are better at distinguishing dark colours, so it's better to allow representing more of those), so to interpolate properly you need to invert that function, interpolate, then reapply. The difference that makes to colour gradients is really striking.

topherclay 2y ago
Do you mean converting the RGB value to LAB values in the CIELAB color space and doing the interpellation there? Is there a better way to do it?
- CarVac 2y ago
  No, you just need to linearize the brightness.
  - drjasonharrison 2y ago
    Typically this is done by using a look-up table to convert the 8-bit gamma encoded intensities to 10-bit (or more) linear intensities. You can use the same look-up table for R, G, and B. Alpha should be linear.

yosefk 2y ago

While this is a valid point, cv2.resize doesn't actually implement color conversion in a way addressing this issue; at least in my testing I get identical results whether I interpret the data as RGB or BGR. So if you want to use cv2.resize, AFAIK you can count on it not caring which channel is which. And if you need fast resizing, you're quite likely to settle for the straightforward interpolation implemented by cv2.resize.

planede 2y ago

Even if the RGB components correspond to sRGB, to linearize you apply the same non-linear function to each component value, independently. So even if you do the interpolation in a linear colorspace, the order of the sRGB components does not matter.