image_entropy — IMGMETRICS `information` op

• Data kinds: image2d → scalar

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

Usage

Shannon entropy of one image [bit].

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

`mutual_information` と同じビン割りで出すので、

`mutual_information(a, a) == image_entropy(a)` が厳密に成り立つ

(テストで固定)。既存の `entropy_gray / entropy_image`(backends)は

別のビン割りなので値は一致しない ―― こちらは同時分布と整合する側。

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

• poc_dehazing — py -3.11 examples/poc_dehazing.py

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

—

Same category (`information`)

joint_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).*

image_entropy — IMGMETRICS information op