information op• Data kinds: image2d × image2d → image2d
• Call: import fullseye as fs; fs.ledger.joint_histogram(a, b, bins=64, data_range=None) (to call the implementation directly, import imgmetrics; imgmetrics.joint_histogram(a, b, bins=64, data_range=None); from the registry, opsimgmetrics.get("joint_histogram"))
Joint histogram of two images (the normalised joint probability).
The bin width is set from data_range -- if it were stretched per image by min/max, a uniformly dark picture and a uniformly bright picture would have the same mutual information.
Procedure: flatten the 2 images, take numpy.histogram2d with the same bin edges [lo, lo + data_range] for both (lo = the smaller of the 2 images' minima, divided into bins equal parts), and divide by the total count to get the joint probability.
• a, b: same shape (any dimensionality), finite. If the dtypes are not the same, give data_range explicitly (inference from mixed dtypes is rejected).
• bins: an integer of at least 2.
• data_range: automatic for integer dtypes (uint8 = 255 etc.); for float, only when in [0, 1] is it 1.0, otherwise it must be given explicitly.
• Return value: float64 of shape (bins, bins), summing to 1. Rows are bins of a, columns bins of b. Values above lo + data_range do not fall into any bin (they are not counted).
• Failure (MetricContractError): shapes differ / empty / non-finite / bins < 2 / 0 samples fall within the range.
joint_entropy / mutual_information / image_entropy are computed from this table.
• 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
image2d as input)mse · rmse · psnr · ssim · ms_ssim · ssim_map · image_entropy · joint_entropy
information)image_entropy · joint_entropy · mutual_information · normalized_mutual_information
*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.