Is there any techniques using wavelet decomposition to decimate the high frequency component while retaining peaks? I feel like that's a more principled approach than sampling but I haven't seen any literature on it describing the specific techniques (unless the idea is fundamentally unsound which is not obvious to me).

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huntergemmer 6 hours ago | parent [-]

Interesting idea - I haven't explored wavelet-based approaches but the intuition makes sense: decompose into frequency bands, keep the low-frequency trend, and selectively preserve high-frequency peaks that exceed some threshold.

My concern would be computational cost for real-time/streaming use cases. LTTB is O(n) and pretty cache-friendly. Wavelet transforms are more expensive, though maybe a GPU compute shader could make it viable.

The other question is whether it's "visually correct" for charting specifically. LTTB optimizes for preserving the visual shape of the line at a given resolution. Wavelet decomposition optimizes for signal reconstruction - not quite the same goal.

That said, I'd be curious to experiment. Do you have any papers or implementations in mind? Would make for an interesting alternative sampling mode.

	▲	vlovich123 2 hours ago \| parent [-]
		I don't. I just remember watching a presentation on it and it always struck me that wavelets are an incredibly powerful and underutilized technique for data reduction while preserving quality in a quantifiable and mathematically justifiable way. I don't have any papers in mind, but I do think that the critique around visual shape vs signal reconstruction may not be accurate given that wavelets are starting to see a lot of adoption in the visual space (at least JPEG2000 is the leading edge in that field). Might also be interesting to use DCT as well. I think these will perform better than LTTB (of course the compute cost is higher but there's also HW acceleration for some of these or will be over time).