I want to find out which algorithm is the best that can be used for downsizing a raster picture. With best I mean the one that gives the nicest-looking results. I know of bicubic, but is there something better yet? For example, I've heard from some people that Adobe Lightroom has some kind of proprietary algorithm which produces better results than standard bicubic that I was using. Unfortunately I would like to use this algorithm myself in my software, so Adobe's carefully guarded trade secrets won't do.
Added:
I checked out Paint.NET and to my surprise it seems that Super Sampling is better than bicubic when downsizing a picture. That makes me wonder if interpolation algorithms are the way to go at all.
It also reminded me of an algorithm I had "invented" myself, but never implemented. I suppose it also has a name (as something this trivial cannot be the idea of me alone), but I couldn't find it among the popular ones. Super Sampling was the closest one.
The idea is this - for every pixel in target picture, calculate where it would be in the source picture. It would probably overlay one or more other pixels. It would then be possible to calculate the areas and colors of these pixels. Then, to get the color of the target pixel, one would simply calculate the average of these colors, adding their areas as "weights". So, if a target pixel would cover 1/3 of a yellow source pixel, and 1/4 of a green source pixel, I'd get (1/3*yellow + 1/4*green)/(1/3+1/4).
This would naturally be computationally intensive, but it should be as close to the ideal as possible, no?
Is there a name for this algorithm?
"The magic kernel" is likely the best image resizing algorithm, with superior results and performance when compared to Lanczos. It is used by both Facebook and Instagram.
More information is available at https://johncostella.com/magic/