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"))
Joint entropy H(A, B) [bit].
Formula: -sum(p log2 p) taken over all cells of joint_histogram(a, b, bins, data_range) (only p > 0).
• a, b: same shape, finite. dtype and data_range are handled as in joint_histogram (for float outside [0, 1] it must be given explicitly).
• bins: an integer of at least 2. The value depends on the number of bins -- the upper bound is 2 log2(bins) (12 bit at the default 64). When comparing with numbers from elsewhere, match bins and data_range.
• Return value: a Python float, in bits, 0 or more. 0 if the 2 images are both uniform. H(A) + H(B) - H(A, B) is the mutual information (mutual_information).
• Failure (MetricContractError): shapes differ / empty / non-finite / bins < 2 / data_range cannot be inferred.
Used as an objective function for alignment (registration), together with mutual_information / normalized_mutual_information.
• 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 · 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.