Conceptual Analysis & Observable Dimensions

1. Scope & Formal Attribution

MotionScore publicly documents an S-to-F grading framework for evaluating animation performance according to rendering characteristics. The following section summarizes the publicly documented concepts relevant to Motion Audit and does not reproduce MotionScore's implementation.

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2. Publicly Documented Conceptual Classification

[PUBLIC] Documented by MotionScore

Public documentation on score.motion.dev/methodology outlines an S-to-F qualitative hierarchy reflecting how deeply animations disturb the browser's render pipeline:

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3. Observable Dimensions Under Investigation

[RESEARCH] SoraLabs Investigation & Research Areas

To evaluate web motion rigorously without assuming unpublished third-party scoring formulas, SoraLabs groups observable animation performance into four technical research dimensions for this investigation:

1. Animation Execution Pipeline:

   • [PUBLIC] Qualitative distinction between compositor, paint, and layout operations.
   • [OBSERVED] Observable CDP traces (Animation.animationStarted, style recalculations, paint rects).
   • [IMPLEMENTATION] Experimental property-cost vector, currently parameterized as $\mathcal{C}_{\text{prop}}$ (initial baseline $\{1, 4, 8\}$) and subject to empirical calibration.

2. Scroll-Driven Mechanics:

   • [PUBLIC] Distinction between off-thread declarative scroll timelines and synchronous script listeners.
   • [OBSERVED] Main-thread event latency, passive flag compliance, and animation frame synchronization.
   • [IMPLEMENTATION] Experimental listener penalty parameterization, currently explored in the range $1.1\times$–$1.3\times$ and subject to benchmark validation.

3. Layout Thrashing & Mutation Patterns:

   • [PUBLIC] Concept of interleaved read/write mutations causing pipeline stall.
   • [OBSERVED] Chromium Blink Document::UpdateStyleAndLayoutTree call stacks in Performance profiles.
   • [IMPLEMENTATION] Experimental graph clustering approach using DisjointSet (Union-Find), with causal thrash-chain escalation.

4. GPU Texture & Memory Pressure:

   • [OBSERVED] Compositor layer quad bounds (LayerTree.layerTreeDidChange), layer counts, and DPR-aware texture and layer estimates.
   • [IMPLEMENTATION] Theoretical uncompressed RGBA8 texture estimate ($\mathcal{M}_{\text{raw}} = W \times H \times 4 \times \text{DPR}^2$) alongside modeled tiled backing-store buffer: $\mathcal{M}_{\text{model}} = W \times H \times 4 \times \text{DPR}^2 \times K_{\text{buffer}}$ where $K_{\text{buffer}} = 3$ is an experimental/reference modeling assumption, not a claim about a universal Chromium allocation strategy.

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4. Contextual Modifiers & Perception Boundaries

Real-world execution cost diverges substantially based on geometric and temporal context:

• Surface Area Scaling:
  • [OBSERVED] Large raster invalidations bottleneck GPU fragment fillrate. Repainting a full-screen background is vastly heavier than repainting an icon.
  • [IMPLEMENTATION] Clamped paint surface scaling ratio ($\mathcal{S}_{\text{paint}} \in [0.25,\, 5.0]$), currently parameterized around a $5\%$ viewport baseline and subject to empirical calibration.
• Visibility & Offscreen Culling:
  • [OBSERVED] Offscreen elements ticking animations consume main-thread execution time and power unnecessarily.
• Timing, Concurrency, and Frame Budgets:
  • [RESEARCH] $16.67\text{ms}$ ($60\text{Hz}$) and $8.33\text{ms}$ ($120\text{Hz}$) are used as reference frame budgets; actual runtime budgets depend dynamically on the display hardware's native refresh rate (ProMotion, $144\text{Hz}$, variable refresh rate).
  • [IMPLEMENTATION] Experimental duration weighting for short-lived interactions ($< 250\text{ms}$), currently parameterized as $\mathcal{D}_{\text{time}} \in [0.25,\, 1.0]$ and subject to empirical refinement.
  • [IMPLEMENTATION] Experimental concurrency aggregation model ($0.8 \cdot P_{75} + 0.2 \cdot \text{Max}$), designed to balance representative interaction density against transient outlier spikes, and targeted for comparative sensitivity analysis against alternate percentiles ($P_{50}, P_{75}, P_{90}, P_{95}, \text{Max}$).