joint_entropy — IMGMETRICS `information` op

• Data kinds: image2d × image2d → scalar

• Call: import fullseye as fs; fs.ledger.joint_entropy(a, b, bins=64, data_range=None) (to call the implementation directly, import imgmetrics; imgmetrics.joint_entropy(a, b, bins=64, data_range=None); from the registry, opsimgmetrics.get("joint_entropy"))

Usage

Joint entropy H(A, B) [bit].

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

式: `-sum(p log2 p) を joint_histogram(a, b, bins, data_range)` の全セル

(`p > 0` のみ)について取る。

• `a, b: 同じ形、有限。dtype と data_range の扱いは joint_histogram`

と同じ(float は `[0, 1]` 以外なら明示必須)。

• `bins: 2 以上の整数。**値はビン数に依存する** ―― 上限は 2 log2(bins)`

(既定 64 で 12 bit)。他所の数値と比べるときは `bins と data_range` を揃える。

• 返り値: Python の `float、単位 bit、0` 以上。2 枚とも一様なら 0。

`H(A) + H(B) - H(A, B) が相互情報量(mutual_information`)。

• 失敗(`MetricContractError): 形が違う / 空 / 非有限 / bins < 2` /

`data_range` を推定できない。

位置合わせ(レジストレーション)の目的関数として、`mutual_information` /

`normalized_mutual_information` と併せて使う。

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 · mutual_information · 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).*

joint_entropy — IMGMETRICS information op