Let's say I have an FFT output that's many, many, bins wide, and I want to compress that information into a narrower display (let's say from 4M bins down to 1024 bins). My approach has been to sum up each set of [4M/1024] bins, and use that as the final output. But should I be averaging across the bins? That is, should I be taking each 3906bin group from the 4M bin wide spectrum and computing an average across those bins, and stuffing it into the appropriate place in the 1024-wide spectrum, or something else? Seems to me that if I have 4000 1Hz-wide bins, I should sum them to give me the total power in a single bin that "represents" the same amount of bandwidth. But is it more subtle than that? -- Marcus L. Principal Investigator, Shirleys Bay Radio Astronomy Consortium http://www.sbrac.org

on 2009-03-09 04:46

on 2009-03-09 16:39

On Sun, Mar 8, 2009 at 6:13 PM, Marcus D. Leech <removed_email_address@domain.invalid> wrote: Seems to me that if I have 4000 1Hz-wide bins, I should sum them to give > me the total power in a single bin that > "represents" the same amount of bandwidth. But is it more subtle than > that? As usual, yes and no. If you're concerned with statistical hygiene, then the mean (averaging over multiple bins) is defensible. And if you wanted to add a dimension to each reduced output bin, like color, you might want to throw in the variance within each set of sub-bins contributing to the average. The most robust estimator would be the median, though, probably -- the exact midpoint between the lowest and highest values in each set of sub-bins. However it sounds like what you're going for is a kind of compression that's lossy but optimizes for visual properties, not statistical robustness. That's usually highly non-linear and very subjective. In that case, why not pick what just looks good on your data? The algorithm that John Ackermann suggests is likely to be as good as anything else. Frank