With pixel data, we have low enough resolutions that our thinking is biased towards the display device the majority of the time; we can author "pixel art" that

With pixel data, we have low enough resolutions that our thinking is biased towards the display device the majority of the time; we can author "pixel art" that expects the results to look square, or very close to square. And when we resize that art, we don't interpolate the color values(unless we want it to look like shit), we use a simple drop-sample or nearest-neighbor. But when we resize photographic images, suddenly we have a reason to do other things. Take for example this Stack Overflow answer on Lanczos resampling. [0] It describes exactly what you say we can't and don't use, but is actually used every day in Photoshop: a filter kernel that produces ringing artifacts on black-white patterns. As I allude to with the pixel art, it depends on the source content. When we get an image off of a camera, it's sampled in a way that assumes the pixels are points as impulses, and thus should be processed "as if" they are convolutions of the sinc function, even though the data itself is "just" points. Digital photographs always look blurry or noisy when examined pixel-by-pixel, and only form coherency to our eyes when viewed more distantly. For similar reasons, pixel artists can't downsample and process arbitrary photos and pass them off as legitimate pixel art, because the original content isn't designed towards the display of square or near-square pixels. [0] http://gis.stackexchange.com/questions/10931/what-is-lanczos-resampling-useful-for-in-a-spatial-context http://gis.stackexchange.com/questions/10931/what-is-lanczos...