Crate viser_metrics 

Metric-vs-metric correlation and divergence analysis for the viser video-encoding-optimizer workspace.

Where viser_quality computes each metric, this crate compares the metrics against each other: how strongly PSNR, SSIM, VMAF, SSIMULACRA2 and butteraugli agree on the same content — via Pearson, Spearman (SROCC) and Kendall tau-b (KROCC) — and which samples they most disagree about (divergence detection).

A “sample” is whatever the aligned series share: per-frame scores within one clip, or one aggregate score per encode across a ladder. The core functions are metric-agnostic and operate on &[f64]; series_from_frames is a convenience bridge from viser_quality::FrameResult.

use viser_metrics::{MetricSeries, correlation_matrix};
let series = vec![
    MetricSeries::new("vmaf", vec![80.0, 85.0, 90.0], true),
    MetricSeries::new("psnr", vec![37.0, 38.0, 40.0], true),
];
let m = correlation_matrix(&series);
assert!((m.spearman[0][1] - 1.0).abs() < 1e-9); // perfectly monotonic

Structs

CorrelationMatrix

Pairwise correlation matrices across a set of labelled metric series.

Divergence

A sample the metrics disagree about, with its per-metric normalized quality.

MetricSeries

One metric’s values across an aligned set of samples (frames or encodes).

Functions

correlation_matrix

Compute pairwise Pearson/Spearman/Kendall correlation across all series.

divergences

Rank samples by how much the metrics disagree about them, worst (largest spread) first.

kendall_tau

Kendall tau-b rank correlation (KROCC), corrected for ties.

pearson

Pearson product-moment correlation of two equal-length series.

series_from_frames

Build per-metric series from per-frame quality results.

spearman

Spearman rank correlation (SROCC) — Pearson on tie-averaged ranks.