Tools Visual Performance
✅ Demo Tier — All tools available in the free Demo tier at
demo.cbrowser.ai/mcp
14 tools in this category.
Catch visual regressions, performance degradations, cross-browser rendering differences, responsive layout breakage, and persona-specific attention patterns.
Built on Wasserstein optimal transport theory — smart baselines compute the barycenter of multiple captures, transport maps show where content moved, and attention analysis models persona-specific saliency using W₂ on CIE-Lab distributions.
Tools
| Tool | Description |
|---|---|
visual_baseline |
Capture a visual baseline for a URL |
visual_regression |
Run AI visual regression test against a baseline |
cross_browser_test |
Test page rendering across multiple browsers |
cross_browser_diff |
Quick diff of page metrics across browsers |
responsive_test |
Test page across different viewport sizes |
ab_comparison |
Compare two URLs visually (staging vs production) |
visual_baseline (captures=5) |
Capture a smart consensus baseline using Wasserstein barycenter. Takes N screenshots, rejects outliers, computes optimal |
visual_regression (auto-detects smart baselines) |
Run visual regression against a smart baseline. Uses Wasserstein distance with adaptive threshold based on observed rend |
transport_map |
Generate a Visual Transport Map showing WHERE visual content moved between two screenshots. Produces heatmap, flow arrow |
attention_analysis |
Analyze where a persona's visual attention goes on a page using Wasserstein saliency. Produces attention alignment, entr |
attention_compare |
Compare attention patterns between two personas on the same page. Shows where they look differently and the Wasserstein |
perf_baseline |
Capture performance baseline for a URL |
perf_regression |
Detect performance regression against baseline with configurable sensitivity. Uses dual thresholds: both percentage AND |
list_baselines |
List all saved baselines (visual and performance) |
Research
- Klein & Frintrop (DAGM 2012) — W₂ saliency on multivariate normals
- Agueh & Carlier (SIAM 2011) — Barycenters in Wasserstein space
- Taylor & Fiebach (2025) — OT predicts neural activity at <225ms
- Bylinskii et al. (IEEE TPAMI 2019) — EMD for saliency evaluation
Related
- Tools Overview — All 120 tools
- Cognitive Optimal Transport — Research foundation
Visual Reports Gallery
All heatmaps, attention overlays, motor overlays, and visual cognitive stories auto-save to your account gallery at cbrowser.ai/account/reports. Filter by tool type, persona, or target URL.