4 ms·

The upscaled image stores more information than the original image, so it must be possible to keep all the information while still doing edge enhancement!

pbhjpbhj 3y ago
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 3y ago
  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 3y ago
  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 3y ago
    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 3y ago
      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.

pbhjpbhj 3y ago

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 3y ago
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 3y ago
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 3y ago
  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 3y ago
    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.