mutual_information — IMGMETRICS `information` op

• Data kinds: image2d × image2d → scalar

• Call: import imgmetrics; imgmetrics.mutual_information(a, b, bins=64, data_range=None) (or opsimgmetrics.get("mutual_information"))

Usage

Mutual information I(A; B) [bit] = H(A) + H(B) - H(A, B).

> The detailed description below is the original text — the summary and the headings are translated.

ビン数に依存する(増やすほど上振れする)ので `bins` は明示的な引数。

独立な 2 枚でも標本が有限なので厳密に 0 にはならない ―― どれくらい上振れ

するかはテストに数値で残してある。

Detailed usage guide

• image_difference_metrics family guide

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• image_quality_metrics — py -3.11 examples/image_quality_metrics.py

Ops the type connects to (they accept `scalar` as input)

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Same category (`information`)

image_entropy · joint_entropy · normalized_mutual_information · joint_histogram

*Provenance: imgmetrics.py — IMGMETRICS operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

mutual_information — IMGMETRICS information op