I think the DCT is a compelling way to interact with spatial information when the channel is constrained. What works for jpeg can likely work elsewhere. The energy compaction properties of the DCT mean you get most of the important information in a few coefficients. A quantizer can zero out everything else. Zig zag scanned + RLE byte sequences could be a reasonable way to generate useful "tokens" from transformed image blocks. Take everything from jpeg encoder except for perhaps the entropy coding step.

At some level you do need something approximating a token. BPE is very compelling for UTF8 sequences. It might be nearly the most ideal way to transform (compress) that kind of data. For images, audio and video, we need some kind of grain like that. Something to reorganize the problem and dramatically reduce the information rate to a point where it can be managed. Compression and entropy is at the heart of all of this. I think BPE is doing more heavy lifting than we are giving it credit for.

I'd extend this thinking to techniques like MPEG for video. All frame types also use something like the DCT too. The P and B frames are basically the same ideas as the I frame (jpeg), the difference is they take the DCT of the residual between adjacent frames. This is where the compression gets to be insane with video. It's block transforms all the way down.

An 8x8 DCT block for a channel of SDR content is 512 bits of raw information. After quantization and RLE (for typical quality settings), we can get this down to 50-100 bits of information. I feel like this is an extremely reasonable grain to work with.