quantize — ONED signal op

• Datenarten: signal → signal

• Aufruf: import fullseye as fs; fs.ledger.quantize(x, bits=8, mode='round', dither=None, seed=0) (die Implementierung direkt: import dsp; dsp.quantize(x, bits=8, mode='round', dither=None, seed=0); aus dem Register: ops1d.get("quantize"))

Verwendung

Skalarer Quantisierer mit angegebenem Fehlermodell, optional mit Dither.

> Die ausführliche Beschreibung unten ist der Originaltext — Zusammenfassung und Überschriften sind übersetzt.

*bits* sets the number of levels L = 2**bits over the signal's own min..max range. *mode* is "round" (mid-tread, unbiased) or "truncate" (floor, the convention PIL's posterize and most fixed-point casts use).

The two differ by more than a rounding convention. With step Delta = range/(L-1) both have error variance Delta**2/12, but truncation also carries a mean of -Delta/2, so its mean square error is

truncate: Delta**2/12 + (Delta/2)**2 = Delta**2/3

round: Delta**2/12

— a factor of 4. Anything that measures a level (not just displays it) must round.

*dither* adds noise before quantising so the error stops being a function of the signal: "tpdf" (triangular, the audio standard — two uniform draws summed, so the error's variance no longer depends on the sample value) or "rpdf" (one uniform draw). Dither raises the total error power but removes the correlation that makes quantisation audible as distortion rather than as hiss. seed fixes the draw so the op stays deterministic.

Applicability. (1) The range is taken from *this* signal, so two signals quantised separately do not share a scale. (2) bits=1 with no dither is a comparator, not a quantiser — the error model does not apply. (3) The error model assumes the signal moves by more than a step between samples; on a flat stretch the error is a constant offset, not noise.

Referenzen (Beispieldaten, Literatur)

• Katalog der Beispieldaten (Download-URLs / Lizenzen) — 2-D nutzt skimage.data (BSD/Public Domain) plus synthetische Bilder, 3-D nennt Download-URLs echter Datenquellen (Stanford, PDS, …).

• Herkunft und Literatur der Operatoren — die Quellen der Forschung/Verfahren, auf denen diese Operatorfamilie beruht.

• Der kanonische Algorithmus (Autor, Jahr) und seine Anwendungen stehen im Familienleitfaden oben.

Ausführbare Beispiele (verifizierte Samples, die diesen Operator wirklich aufrufen)

• poc_cold_chain_excursion — py -3.11 examples/poc_cold_chain_excursion.py

Typkompatible Folge-Operatoren (nehmen signal als Eingabe)

create_funct_1d_array · create_funct_1d_pairs · smooth_funct_1d_gauss · smooth_funct_1d_mean · derivate_funct_1d · integrate_funct_1d · zero_crossings_funct_1d · local_min_max_funct_1d

Gleiche Kategorie (signal)

lowpass · highpass · bandpass · envelope · rms · local_std · companding_mu_law · resample


*Provenance: dsp.py — ONED Operator-Registry. Diese Notiz wird von tools/opdocs.py md erzeugt (nicht von Hand bearbeiten).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.