✨ v0.10.0

[0.10.0] - 2026-02-03

What's Changed

Added

• GMSD (Gradient Magnitude Similarity Deviation) - New includeGMSD option for fast, edge-sensitive perceptual comparison
  • Returns gmsdScore in results (0.0 = identical, higher = more different)
  • Very fast compared to SSIM, ideal for detecting border/outline changes
  • Based on Xue et al. 2014 research paper
  • Note: GMSD requires images with identical dimensions. For variable-height comparisons, gmsdScore will be null
• Cluster Merging - New clusterMerge option to consolidate fragmented text regions
  • Simple API: clusterMerge: true enables with sensible defaults
  • Advanced API: Pass an object with horizontalDistance, yBandTolerance, maxHeightRatio, maxWidthRatio
  • Solves the "59 clusters for one date string" problem by intelligently merging nearby character-level changes into logical text regions
  • Uses SWT-inspired heuristics (Epshtein et al. 2010) for horizontal-biased text detection

Full Changelog: v0.9.0...v0.10.0

✨ v0.9.0

[0.9.0] - 2026-01-26

What's Changed

Added

• Diff Fingerprinting API - New functionality for grouping similar diffs across different image comparisons. This enables batch-approving the same visual change that appears on multiple pages (e.g., header/footer updates).
  • computeFingerprintSync() - Generate a compact fingerprint from a diff result
  • fingerprintSimilaritySync() - Compare two fingerprints with a similarity score (0.0-1.0)
  • fingerprintHashSync() - Get a coarse hash for fast O(1) grouping
  • DiffFingerprint interface with rich metadata:
    • zoneMask - 4x4 spatial grid showing which regions are affected
    • clusterCount, clusterPositions, clusterSizes - Spatial distribution data
    • diffMagnitude - Bucketed size categories (tiny/small/medium/large/massive)
    • avgIntensity, avgDensity - Change characteristics
    • hash - Pre-computed coarse hash for grouping

Full Changelog: v0.8.1...v0.9.0

✨ v0.8.1

[0.8.1] - 2026-01-09

What's Changed

Changed

• Performance: SSIM calculation is now 5x faster using integral images and parallel processing (#21)
  • Full HD (1920×1080) images: 239ms → 51ms (4.7× speedup)
  • 640×480 images: 35ms → 8.3ms (4.2× speedup)
• Performance: Skip SSIM calculation for identical images, saving ~250M operations per comparison (#20)
  • When images have no differences (or only anti-aliasing artifacts), SSIM returns 1.0 immediately
  • Particularly beneficial for visual regression test suites where most screenshots match their baselines

Fixed

• Clean up duplicate entries in package.json files list

Full Changelog: v0.8.0...v0.8.1

✨ main

Release vmain