4 ms·
You’re thinking along the right lines, but the challenge is that a raw diff will have the same number of pixels as the raw image, so no compression in bandwidth
by ssivark 6y ago
You’re thinking along the right lines, but the challenge is that a raw diff will have the same number of pixels as the raw image, so no compression in bandwidth. So, how do we represent the diff/residue also with fewer numbers? At that point it’s the same as choosing better parameters within some clever encoding (be it pre-designed like JPEG or H.264 or learned via ML).
- alkonaut 6y agoI was thinking that subtracting the predicted image would give an image that has more zeroes and compresses better (much like dct+quantization for jpeg). After all, any time the neural network would predict an area of the image almost exactly, it can be omitted from the diff stream completely too.
- ssivark 6y agoThe diff will be low norm (for some suitable norm), but it needn’t be sparse in pixel space. It could be sparse in some other spaces (Eg: wavelet basis), but that’s right where the challenge lies — finding a basis where the data (and/or the residue) to be transmitted is sparse.