ms_ssim — IMGMETRICS fidelity op

• Data kinds: image2d × image2d → scalar

• Call: import fullseye as fs; fs.ledger.ms_ssim(a, b, data_range=None, win_size=11, sigma=1.5, K1=0.01, K2=0.03, weights=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333), crop_border=True) (to call the implementation directly, import imgmetrics; imgmetrics.ms_ssim(a, b, data_range=None, win_size=11, sigma=1.5, K1=0.01, K2=0.03, weights=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333), crop_border=True); from the registry, opsimgmetrics.get("ms_ssim"))

Usage

Multi-scale SSIM (Wang, Simoncelli & Bovik, Asilomar Conf. 2003).

It goes through 5 levels of downsampling, so for an 11-pixel window to be valid at the last level, 176 pixels per side are required. When that is not met, the number of levels is not silently reduced -- an MS-SSIM with a different number of levels is a different metric, and comparing them would be a lie. If too small, ValueError.

Only for 2-D grey images (for colour, call it per channel rather than via channel_axis).

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

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

luma_limited_u8 · rank_data · tid2013_index

Same category (fidelity)

mse · rmse · psnr · ssim · ssim_map

*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.