image_entropy — IMGMETRICS information op

• Data kinds: image2d → scalar

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

Usage

Shannon entropy of one image [bit].

It uses the same binning as mutual_information, so mutual_information(a, a) == image_entropy(a) holds exactly (pinned by a test). The existing entropy_gray / entropy_image (backends) use a different binning, so their values do not match -- this one is the side consistent with the joint distribution.

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)

luma_limited_u8 · rank_data · tid2013_index

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).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.