Non-frequency domain because it seems to me preserving feature legibility is a useful property. I can say “rectangle id 14 contains a smiley”
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Example of why it’s useful: you can navigate (inefficiently) directly on a compressed map if it’s not in some other conformal space. Like knowing only the highways topology. Shortest path point to point in the highway graph won’t be real shortest path but it will avoid obstacles.https://twitter.com/vgr/status/1313564520925687808 …
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I’m trying to think this through for time perception in narrative memory, not actually implement it. For example our brains seem to store emotionally charged events in great detail (so recalled time perception slows per David Eagleman experiments) but yada-yada over white space.
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Ah! Run-length encoding seems closest to what I’m trying to model. Thanks
@joshu ... it’s the yada-yada montage algorithm. https://en.wikipedia.org/wiki/Run-length_encoding …Show this thread -
Ah, compressive sensing seems to be in the same spirithttps://twitter.com/atduskgreg/status/1313567310372696066 …
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Good statement of the problem, which I’d compress further to “explainable compressions.” Specifically ones that in the limit recover the raw ontology of explanation. The way a caricature admits a “facial” explanation of a face similar to a photograph.https://twitter.com/sam_havens/status/1313570087396282369 …
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For time, slow/fast time perception is still ontologically *time* perception. We don’t suddenly switch to frequency based perception when thinking about, for eg., 10,000 years of yada-yada history. Cyclic theories of history seem clearly like a different compression ontology.
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I tried to replicate a lossless compression paper from like fifteen years ago that looked for "motifs" in non-frequency-domain. Not a well known technique but there's stuff out there, fwiw. Not sure about well known.
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could only get like 0.5% compression iirc lol
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Superpixel?
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Hadn’t heard the term... are there compression algorithms based on super pixels?
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