> While describing a pixel as a little square is frowned upon in a world of signal processing, in some contexts it is a useful model that lets us calculate an accurate coverage of a pixel by the vector geometry
I wish people would try to calculate the coverage of some other reconstruction filter instead of just separate little squares. I’m not convinced it would be more expensive, and would hopefully give better results (less griddy artifacts, better spatial resolution), especially for images that will be rotated or resampled later.
If we assumed a radially symmetric kernel, then coverage would just be some monotonic function of the distance from the pixel center to the edge, and this function could be approximated using some low-degree polynomial and computed very quickly.
I wish people would try to calculate the coverage of some other reconstruction filter instead of just separate little squares. I’m not convinced it would be more expensive, and would hopefully give better results (less griddy artifacts, better spatial resolution), especially for images that will be rotated or resampled later.
If we assumed a radially symmetric kernel, then coverage would just be some monotonic function of the distance from the pixel center to the edge, and this function could be approximated using some low-degree polynomial and computed very quickly.
(Maybe this has been done?)