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I do wonder if all the new AI-based "upscaling" algorithms, which can fill in detail to make an image larger, can be used to produce a compression-decompression

by SamBam 5y ago

I do wonder if all the new AI-based "upscaling" algorithms, which can fill in detail to make an image larger, can be used to produce a compression-decompression algorithm: Perform a heavily lossy compression, then "upscale" the result and see if it returns to a good-enough match. If so, compress even further, until your upscaling no longer gives you a good-enough match. Like regular JPG encoding, this can be set to an arbitrary level of "good enough."

zone411 5y ago
That's pretty much how AI image compression works - you're describing standard autoencoder (encoder/decoder) architecture with some adaptiveness.
IIAOPSW 5y ago
Sufficiently good compression is necessarily dimensionality reduction. Out of compression AI can be jerryrigged, and out of AI compression can be jerryrigged. This truth may be relevant to the evolutionary history of natural intelligence.
yobbo 5y ago
This has been tried in research and works, with typical artefacts such as horses getting zebra-stripes if they stand in tall grass (or something like that ...)
- eru 5y ago
  That sounds very similar to Dürer's Rhinoceros. Or in general, learned people trying to draw from even detailed descriptions. https://en.wikipedia.org/wiki/D%C3%BCrer%27s_Rhinoceros https://en.wikipedia.org/wiki/D%C3%BCrer%27s_Rhinoceros > Dürer's Rhinoceros is the name commonly given to a woodcut executed by German painter and printmaker Albrecht Dürer in 1515.[1] The image is based on a written description and brief sketch by an unknown artist of an Indian rhinoceros that had arrived in Lisbon in 1515.[2] Dürer never saw the actual rhinoceros, which was the first living example seen in Europe since Roman times.
hansvm 5y ago
This works, and there's also a fun variant where you then losslessly compress the residual between ground truth and the up-sampled image. Since most of the information was encoded in the NN and already exists at both ends, the data requirements in between are modest.

by SamBam 5y ago

zone411 5y ago

That's pretty much how AI image compression works - you're describing standard autoencoder (encoder/decoder) architecture with some adaptiveness.

IIAOPSW 5y ago

Sufficiently good compression is necessarily dimensionality reduction. Out of compression AI can be jerryrigged, and out of AI compression can be jerryrigged. This truth may be relevant to the evolutionary history of natural intelligence.

yobbo 5y ago

This has been tried in research and works, with typical artefacts such as horses getting zebra-stripes if they stand in tall grass (or something like that ...)

eru 5y ago
That sounds very similar to Dürer's Rhinoceros. Or in general, learned people trying to draw from even detailed descriptions. https://en.wikipedia.org/wiki/D%C3%BCrer%27s_Rhinoceros https://en.wikipedia.org/wiki/D%C3%BCrer%27s_Rhinoceros > Dürer's Rhinoceros is the name commonly given to a woodcut executed by German painter and printmaker Albrecht Dürer in 1515.[1] The image is based on a written description and brief sketch by an unknown artist of an Indian rhinoceros that had arrived in Lisbon in 1515.[2] Dürer never saw the actual rhinoceros, which was the first living example seen in Europe since Roman times.

hansvm 5y ago

This works, and there's also a fun variant where you then losslessly compress the residual between ground truth and the up-sampled image. Since most of the information was encoded in the NN and already exists at both ends, the data requirements in between are modest.