Does the image hold?
The Lens Structural Index (LSI) doesn’t ask if an image looks like the dataset. It asks if it has structural pressure.
The LSI measures not resemblance but resilience under pressure. It asks whether an image has structure and if it can survive structural drift, recursion collapse and rebuilding, not just if it looks good once.
The Problem
Traditional metrics like FID, CLIP, and SSIM measure fidelity to data, pixels, or prompts. They tell you whether an image looks close enough to its source, but never whether it holds together as a composition. LSI closes that blind spot by treating images as systems under strain.
What LSI Measures
LSI analyzes an image through five geometric primitives: Δx (centroid drift), rᵥ (void ratio), ρᵣ (rupture proxy), θ (tilt), and dₛ (skeleton depth). These combine into three structural scores — Stability (S), Consequence (K), Recursion (R) — which roll into a single LSI100 composite. It’s not about polish; it’s about whether the form survives stress.
Using the two figure set above, the Standard ML Metrics signal dataset fidelity, prompt alignment, and pixel-level similarity. Figure 1 reads “better” by ML metrics (closer to data, smoother, safer), the second a little lesser so. Both figures “look right” to these metrics, with only minor differences between the frontal and angled drawings. From an ML standpoint, both are high-quality, prompt-matching images.
To contrast, LSI Metrics sees figure 1 is stable but hollow (high S, low K). Image 2 wobbles but finds consequence (lower S, higher K, stronger LSI100) and reveal the structural trade-off.
The first figure scores higher in stability (S=0.85) and recursion (R=0.78), meaning its form locks in securely. But its consequence is low (K=0.10), showing it doesn’t engage strain bands, the composition is steady but inert.
The angled study sacrifices some stability (S=0.55) but gains in consequence (K=0.42), as voids, tilt, and rupture create stronger strain engagement. Its composite (LSI100=0.56) edges higher because it carries structural consequence even if it jitters more.
This comparison illustrates why Lens Structural Index (LSI) is complementary to standard ML metrics rather than contradictory. Taken together, ML metrics say both images are good replicas of data and prompts, while LSI adds: one is safe but static, the other unstable but more structurally alive.
Key takeaway: ML metrics ask: Does it look like the data? LSI asks: Does it hold as form under pressure?
Why It Matters
For researchers: LSI reveals why models with identical FID diverge in long runs.
For artists/designers: It offers real-time feedback on why an image feels unstable or hollow.
For QA/testing: It detects collapse states that other metrics smooth over. In every case, LSI makes drift and failure visible as data, not noise.
Interpretive Bands
LSI produces a 0–100 score. Below 40 marks collapse; 40–60 signals drift or instability; 60–80 shows strong but not perfect structure; and 80+ signals rare resonance. These bands help distinguish between surface-stable images and those that carry lasting consequence.
MTN1: Classic centroid center “hold” = peak + reflection act as a single body; gravity read dominates.
MTN2: Shore and foreground elements start steering; the mass feels slightly smaller relative to the lake.
MTN3: The scene is ordered from higher, shoreline geometry and reservoir void take over the weight; grandeur persists as image, not as structure, the planes become disjointed.
Composite (LSI100 = 46): Collapse-band composite. Structure sits below resonance zone; closer to erosion than to recovery —> MTN3 begins to subtly distort.
Differentiator
Other metrics measure fidelity to data. LSI measures fidelity to form. It doesn’t optimize for resemblance, it tests whether composition, voids, and rupture patterns withstand recursive pressure. That’s why it exposes differences other benchmarks can’t see.
It is a diagnostic tool, not aesthetic judgment. LSI quantifies what art directors intuitively understand: dynamic balance (Δx), breathing space (rv), structural breaks (ρr), and compositional gravity. Other metrics can't articulate these principles mathematically.
From default to critique to score to rebuild to consequence
LSI bridges the gap between perceptual quality and structural consequence, enabling evaluations of whether an image is merely “accurate,” or if it truly holds as form.