Aha, no! Downscaling *into a discrete space by an arbitrary amount* is absolutely filling in missing information.
Take the naive case where you downscale a line of four pixels to two pixels - you can simply discard two of them so you go from `0,1,2,3` to `0,2`. It looks okay.
But what happens if you want to scale four pixels to three? You could simply throw one away but then things will look wobbly and lumpy. So you need to take your four pixels, and fill in a missing value that lands slap bang between 1 and 2. Worse, you actually need to treat 0 and 3 as missing values too because they will be somewhat affected by spreading them into the middle pixel.
So yes, downscaling does have to compute missing values even in your naive linear interpolation!
Take the naive case where you downscale a line of four pixels to two pixels - you can simply discard two of them so you go from `0,1,2,3` to `0,2`. It looks okay.
This is already wrong, unless the pixels are band-limited to Nyquist/4. Trivial example where this is not true:
For downscaling, area averaging is simple and makes a lot of intuitive sense and gives good results. To me it's basically the definition of downscaling.
Like yeah, you can try to get clever and preserve the artistic intent or something with something like seamcarving but then I wouldn't call it downscaling anymore.
Comments
Aha, no! Downscaling *into a discrete space by an arbitrary amount* is absolutely filling in missing information.
Take the naive case where you downscale a line of four pixels to two pixels - you can simply discard two of them so you go from `0,1,2,3` to `0,2`. It looks okay.
But what happens if you want to scale four pixels to three? You could simply throw one away but then things will look wobbly and lumpy. So you need to take your four pixels, and fill in a missing value that lands slap bang between 1 and 2. Worse, you actually need to treat 0 and 3 as missing values too because they will be somewhat affected by spreading them into the middle pixel.
So yes, downscaling does have to compute missing values even in your naive linear interpolation!
This is already wrong, unless the pixels are band-limited to Nyquist/4. Trivial example where this is not true:
If such a signal is decimated by 2 you get Which is not correct.For downscaling, area averaging is simple and makes a lot of intuitive sense and gives good results. To me it's basically the definition of downscaling.
Like yeah, you can try to get clever and preserve the artistic intent or something with something like seamcarving but then I wouldn't call it downscaling anymore.
I suggest to read up on this:
https://entropymine.com/imageworsener/pixelmixing/
Hmm, maybe I was wrong then!