Spend Bits Where the Renderer Looks: Observability-Guided Rate–Distortion Optimization for Dynamic Gaussian Splatting Compression

Compressing a trained dynamic Gaussian representation requires understanding both how parameter perturbations affect rendered images and when a primitive's contribution can be transferred to a persistent substitute. We introduce **ORDO-GS** (*Observability-guided Rate–Distortion Optimization*), a post-training compression framework informed by *render-space observability*: the expected sensitivity of rendered images to perturbations of the trained representation. Our analysis identifies a pronounced storage–observability mismatch: positions occupy little storage but require high precision to preserve rendering quality, whereas appearance dominates storage and can be compressed more aggressively. ORDO-GS combines render-aware structural reduction with subsequent attribute-specific compression. Structural reduction combines reconstruction-aware importance with trajectory compatibility to determine which primitives to retain and which contributions to transfer. Persistent co-location motivates complementary prune-only and unified delete–merge reduction branches, selected by a lightweight no-fine-tuning probe. For attribute compression, we derive a position-precision prior from a local render-distortion model and adopt channel-specific compact representations for temporal and appearance attributes. At matched primitive counts, ORDO-GS improves OMG4 by 0.30 dB while reducing storage by 12.6% on N3DV, and by 0.73 dB with 3.4% less storage on SelfCap. ORDO-GS also transfers to Ex4DGS, achieving consistent rate–distortion improvements across representations.