Made a little thing to take fast approximate multivariate medians, at no small cost to my sanity, and it turns out that when I apply it to pixels in California Landscapes time lapse videos (e.g., https://onewilshire.la/@CALandscapeBot/110699293237082262) it’s mostly just Compression Artifact Finder 2023 Pro Edition.
A wonderful thing about image processing is you’re always discovering completely new and fascinating ways to make pictures look worse.
Anyway, the trick is, you take a low discrepancy sequence on a sphere (I used R[2] and a Lambert azimuthal projection) and use those angles as lines to sample depth – a measure of centrality – along; you map these angle samples over your points and reduce with min(), and you get an approximation of Tukey’s multivariate depth, which is a generalization of the median (or quantile). I may explain this better eventually but then again I may not.
And I should say: the way this actually works in practice is you take the timeseries of each pixel in the video, in oklab or your preferred color space, and do an interquartile (or similarly trimmed) mean on it.