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Quantum-realm level normalisation optimization (#965)

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This saves up to 1-2% CPU useage on a PI 4 depending on how much normalisation is actually being done.

* We don't need to test against EPSILON. The factor will never be over 1.0 in basic normalisation mode.
* Don't check the normalisation mode EVERY sample.
* Do as little math as possible by simplfiying all equations as much as possible (while still retaining the textbook equations in comments).
* Misc cleanup
This commit is contained in:
Jason Gray 2022-02-13 15:50:32 -06:00 • committed by GitHub
parent cb194cfd3b
commit 616809b64c
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3 changed files with 96 additions and 97 deletions
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6
playback/src/dither.rs
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@ -3,7 +3,7 @@ use rand::SeedableRng;
use rand_distr::{Distribution, Normal, Triangular, Uniform};
use std::fmt;
const NUM_CHANNELS: usize = 2;
use crate::NUM_CHANNELS;
// Dithering lowers digital-to-analog conversion ("requantization") error,
// linearizing output, lowering distortion and replacing it with a constant,
@ -102,7 +102,7 @@ impl GaussianDitherer {
pub struct HighPassDitherer {
active_channel: usize,
previous_noises: [f64; NUM_CHANNELS],
previous_noises: [f64; NUM_CHANNELS as usize],
cached_rng: SmallRng,
distribution: Uniform<f64>,
}
@ -111,7 +111,7 @@ impl Ditherer for HighPassDitherer {
fn new() -> Self {
Self {
active_channel: 0,
previous_noises: [0.0; NUM_CHANNELS],
previous_noises: [0.0; NUM_CHANNELS as usize],
cached_rng: create_rng(),
distribution: Uniform::new_inclusive(-0.5, 0.5), // 1 LSB +/- 1 LSB (previous) = 2 LSB
}