information op• Data kinds: image2d × image2d → scalar
• Call: import fullseye as fs; fs.ledger.mutual_information(a, b, bins=64, data_range=None) (to call the implementation directly, import imgmetrics; imgmetrics.mutual_information(a, b, bins=64, data_range=None); from the registry, opsimgmetrics.get("mutual_information"))
Mutual information I(A; B) [bit] = H(A) + H(B) - H(A, B).
It depends on the number of bins (the more bins, the more it is biased upward), so bins is an explicit argument. Even for 2 independent images it is not exactly 0 because the sample is finite -- how much it is biased upward is recorded numerically in the tests.
• image_difference_metrics family guide
• 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.
• image_quality_metrics — py -3.11 examples/image_quality_metrics.py
scalar as input)luma_limited_u8 · rank_data · tid2013_index
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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.