temporal op• Data kinds: video → table
• Call: import fullseye as fs; fs.ledger.band_snr(video, f_lo, f_hi, fps) -> 'dict' (to call the implementation directly, import motionmag; motionmag.band_snr(video, f_lo, f_hi, fps) -> 'dict'; from the registry, opsmotionmag.get("band_snr"))
Measure what a clip's temporal band contains, and what it costs -> dict.
Every quantity is a measured mean-square power obtained from the per-pixel temporal DFT (Parseval-normalised so that the bins of one pixel sum to that pixel's mean square), averaged over pixels:
• static_power — the DC bin. The scene that is simply *there*.
• band_power — the bins inside [f_lo, f_hi]. Coherent motion plus whatever noise happens to fall in the band.
• out_of_band_power / out_of_band_bins — everything else except DC. With broadband sensor noise this is the noise floor, and noise_power_per_bin is its per-bin density.
• noise_in_band = noise_power_per_bin * band_bins — how much of band_power is expected to be noise.
• motion_power = max(band_power - noise_in_band, 0) and motion_snr_db = 10*log10(motion_power / noise_in_band).
• image_snr_db = 10*log10(static_power / (band_power + out_of_band_power)) — the static scene against everything that flickers.
The two SNRs answer different questions and magnification moves only one of them. Scaling the in-band phase by alpha scales the in-band motion *and* the in-band noise by the same factor, so the true motion SNR cannot
improve: magnification never makes a measurement more certain than the recording was. What does change is image_snr_db, because the temporal fluctuation of the output frames grows like alpha^2 while the static scene does not.
A caveat that matters when this is run on an already-magnified clip. motion_snr_db here divides the in-band signal by a noise floor estimated from the *out-of-band* bins, and magnification does not touch those. Applied to a magnified video it therefore credits alpha^2 more in-band power against an unchanged noise estimate and reports an improvement that did not occur — measured, +6.86 dB at alpha = 2 on a clip whose true motion SNR cannot have moved. motion_magnify knows the gain and returns the corrected figure as motion_snr_out_db; use that one, not result["snr_out"]["motion_snr_db"].
snr_clamped is True when a reported dB hit the [-100, +100] window (a noiseless synthetic has zero out-of-band power, which is a division by zero rather than an infinite SNR).
• motion_magnification family guide
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• motion_magnification — py -3.11 examples/motion_magnification.py
• poc_motion_magnification — py -3.11 examples/poc_motion_magnification.py
table as input)temporal)temporal_bandpass · temporal_band_power
*Provenance: motionmag.py — MOTIONMAG operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.