[pbhjpbhj]

Stores more data, but the same information, and if there is any interpolation then some of the data is modified, meaning that you lose a little data. In fact even without interpolation I think you change the data.

If you imagine a hard edge that aligns with a pixel [] boundary then you imagine upscaling in various ways, I think it's QED. You change data about the sharpness of the edge.

[] I use an Android phone, with Google's keyboard it genuinely rendered "pixel" with a capital letter. I've never written about Google's device of that name. Silly Google.

[im3w1l]

Well consider nearest neighbor upscaling. Since we are upscaling, every pixel in the source image will determine one our more pixels in the result image. Consider one of the source pixels that turns into multiple result pixels. If you manipulate one of those while leaving the other(s) intact you can still recover the source image (assuming you know which pixels are still good), meaning no data was lost.

[OJFord]

Surely anything other than duplicating each pixel x times both horizontally and vertically (so 1 turns into 4, or 9, or 16, ...) adds information?

(This submission is going to have me reaching for my old textbooks.. about time really!)

[geon]

No. Nearest neighbor, bilinear bicubic etc. is just encoding the same information in different ways.

You could add noise, or generate new details with an ai upscaler. That would create new information.

[OJFord]

Ah, right, anything that's a function purely of what's in the image - no randomness, no external context/'knowledge' to interpret it semantically, is as you say 'encoding the same information in different ways'?

If it can be computed (deterministically) from the image alone, then it was already there.