motion op• Data kinds: video → table
• Call: import fullseye as fs; fs.ledger.riesz_motion_magnify(video, alpha, f_lo, f_hi, fps, scales: 'int' = 4) -> 'dict' (implementation को सीधे call करना हो तो import quatimage; quatimage.riesz_motion_magnify(video, alpha, f_lo, f_hi, fps, scales: 'int' = 4) -> 'dict', registry से लेना हो तो opsquat.get("riesz_motion_magnify"))
Riesz वाले रास्ते से clip की in-band motion को *alpha* से scale करता है। → dict।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
The Riesz-pyramid magnifier of Wadhwa et al. (2014), and the direct counterpart of motionmag.motion_magnify: same contract, same alpha convention (a displacement gain — 1 is the identity, 2 doubles the motion, -1 reverses it), same honesty block, different decomposition.
Each radial sub-band is turned into a monogenic signal, projected onto the band's temporal-mean orientation to give a complex analytic signal z, and the temporal phase deviation angle(z * conj(z_mean)) is band-passed and multiplied by alpha - 1. The band is then re-rendered as I*cos(shift) - R_proj*sin(shift) — the real part of z * exp(i*shift) — and the bands are summed. Because the radial filters are an *amplitude* partition of unity, that sum is the reconstruction: at alpha = 1 the output equals the input to 5.55e-16 (measured on a 64x64x64 clip; motionmag.motion_magnify gives 7.77e-16 on the same clip).
The gain really is the gain. Measuring the magnified clip's displacement with the *independent* steerable estimator motionmag.displacement_series, on a single-grating clip of true amplitude 0.1 px:
======== ========================== ==========================
alpha Riesz measured gain steerable measured gain
======== ========================== ==========================
0.0 0.000000000000 0.000000000000
2.0 2.000000000000 2.000000000000
4.0 4.000000000000 4.000000000000
-1.0 -1.000000000000 -1.000000000000
20.0 20.000000000000 20.000000000000
======== ========================== ==========================
— twelve decimal places, for both, including the reversal.
Returns the same shape of dict motionmag.motion_magnify returns — {"video", "alpha", "band_hz", "fps", "scales", "snr_in", "snr_out", "image_snr_change_db", "motion_snr_out_db", "motion_snr_change_db", "band_power_ratio", "phase_shift_max_rad", "phase_shift_rms_rad", "linear_regime", "reference_coherence"} — and it is the same dict because the SNR block is computed by calling motionmag.band_snr rather than re-deriving it. Two magnifiers that disagree about how to measure their own cost cannot be compared, so they share the measurement.
Magnification never improves the motion SNR, here as there: scaling the in-band phase scales the in-band noise by the same factor. What degrades is the image SNR. Measured on the shared 64x64x64 / 32 fps / 0.2 px / 4 Hz synthetic under sigma = 0.01 noise, band 3-5 Hz, against motionmag.motion_magnify on the identical clip:
====== ================== ================== ============== ==============
alpha image change (dB) image change (dB) band ratio band ratio
Riesz steerable Riesz steerable
====== ================== ================== ============== ==============
2 -4.8611 -4.8260 0.937704 0.935433
4 -10.3616 -10.3504 0.861162 0.858130
8 -15.3515 -15.5097 0.629948 0.628597
====== ================== ================== ============== ==============
The two magnifiers cost essentially the same — within 0.16 dB and 0.3 % of band-power linearity at every gain. So the choice between them is not about magnification quality; it is about the displacement measurement (where the Riesz route has a 13 % failure mode on multi-orientation texture, see riesz_displacement) and about cost (this one is 2.09x faster on the same clip: 0.1034 s against 0.2163 s, best of 7).
Raises ValueError: *video* is not a valid (T, H, W) clip or is
over MAX_PYRAMID_ELEMENTS; |alpha| is over MAX_ALPHA; the pass-band is empty, reaches DC, or exceeds Nyquist; *scales* is outside [1, MAX_SCALES].
• quaternion_monogenic family guide
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• quaternion_monogenic — py -3.11 examples/quaternion_monogenic.py
table को input के रूप में लेते हैं)—
motion)riesz_displacement · riesz_displacement_series
*Provenance: quatimage.py — QUAT operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.