The article mixes up what is significant. The headline refers to an utterly irrelevant result. As others have pointed out, it's just saying that a compression m

The article mixes up what is significant. The headline refers to an utterly irrelevant result. As others have pointed out, it's just saying that a compression method with a large dictionary can beat a (better) compression method with a small dictionary. The interesting bit is that it can transfer a dictionary-ish thing from the text to the audio and image domains and it'll work well enough to beat format-specific compression algorithms. Which is surprising. I'm sure it's only benefiting from a small subset of the 280GB raw text-domain dictionary. But it is surprising that it is not only benefiting, it is benefiting enough to beat the special-purpose compressors. I'll be sad if this turns out to only be true because of some meaningless category of edge cases. On a side note, it does make one wonder if we should have a well-known massive image-based dictionary that compression algorithms can assume the existence of (and request ranges of from a server). Or heck, given these results in the article, maybe it would be a dictionary with a mishmash of different domains. Then when encoding for limited devices with network connections, you'd specify the maximum amount of data it could retrieve from the dictionary (perhaps as a percentage of the compressed size?).