If we substitute "information" for "image", "low information" for "low resolution" and "high information" for "high resolution", perhaps compression could be obtained generically on any data (not just images) by taking a high information bitstream, using a CNN or CNN's (as per this paper) to convert it into a shorter, low information bitstream plus a tensor, and then an entropy (difference) series of bits.

To decompress then, reverse the CNN on the low information bitstream with the tensor.

You now have a high information bitstream which is almost like your original.

Then use the entropy series of bits to fix the difference. You're back to the original.

Losslessly.

So I wonder if this, or a similar process can be done on non-image data...

But that's not all...

If it works with non-image data, it would also say that mathematically, low information (lower) numbers could be converted into high information (higher) numbers with a tensor and entropy values...

We could view the CNN + tensor as mathematical function, and we can view the entropy as a difference...

In other words:

Someone who is a mathematician might be able to derive some identities, some new understandings in number theory from this...

Convolution only works on data that is spatially related, meaning data points that are close to each other are more related than data points that are far apart. It doesn't give meaningful results on data like spreadsheets where columns or rows can be rearranged without corrupting the underlying information.

If by non-image data you mean something like audio, then yes it could probably work.