gmsd_map — IMGMETRICS perceptual op

• Data kinds: image2d × image2d → image2d

• Call: import fullseye as fs; fs.ledger.gmsd_map(ref, dist, data_range: 'float' = 255.0, c: 'float' = 170.0, down_step: 'int' = 2) -> 'np.ndarray' (to call the implementation directly, import iqafsim; iqafsim.gmsd_map(ref, dist, data_range: 'float' = 255.0, c: 'float' = 170.0, down_step: 'int' = 2) -> 'np.ndarray'; from the registry, opsimgmetrics.get("gmsd_map"))

Usage

GMS map (2 m_r m_d + c)/(m_r² + m_d² + c) ∈ (0, 1] (Eq. 3 of the paper). m = magnitude of the Prewitt ([1 0 −1]×3 /3) gradient; preprocessing is a 2×2 mean + decimation by 2 (_average_downsample, the original implementation's 'same' convention). Input (H, W) or (H, W, 3) (3-ch uses Y = 0.299R+0.587G+0.114B, the authors' convention). Raises ValueError: shapes differ, c ≤ 0, down_step < 1.

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)

• poc_iqa_fsim_gmsd_vif — py -3.11 examples/poc_iqa_fsim_gmsd_vif.py

Ops the type connects to (they accept image2d as input)

mse · rmse · psnr · ssim · ms_ssim · ssim_map · image_entropy · joint_entropy

Same category (perceptual)

fsim · fsimc · fsim_pair · gmsd · vifp · phase_congruency_pc

*Provenance: iqafsim.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.