iqa op• Data kinds: any × table → table
• Call: import fullseye as fs; fs.ledger.tid2013_by_distortion(values, index: 'dict', idx=None) -> 'List[dict]' (to call the implementation directly, import iqatid; iqatid.tid2013_by_distortion(values, index: 'dict', idx=None) -> 'List[dict]'; from the registry, opsimgmetrics.get("tid2013_by_distortion"))
Rank correlation between the metric and the MOS for each distortion type (24).
values are the values corresponding to idx (ledger row numbers; omitted = all 3000 rows). Each row: type (1-based), name (readme TABLE I), n, n_inf, srocc, krocc (τ_b). 1 type = 25 references × 5 levels = 125 pairs. Types with fewer than 2 pairs give nan. For distortion 18 (change of colour saturation), the Y′ PSNR is inf (tied) in 106 / 125 pairs, so that type's rank correlation comes out low -- not a defect of the metric but a limitation of a metric that looks only at luminance. Raises ValueError: length mismatch, nan.
• 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.
• poc_iqa_fsim_gmsd_vif — py -3.11 examples/poc_iqa_fsim_gmsd_vif.py
• poc_iqa_tid2013 — py -3.11 examples/poc_iqa_tid2013.py
table as input)luma_limited_u8 · rank_data · tid2013_index
iqa)luma_limited_u8 · rank_data · rank_spearman · rank_kendall_b · tid2013_published · tid2013_root · tid2013_index · tid2013_metric_values
*Provenance: iqatid.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.