Frequency-Decoupled Compression for 3D Gaussian Splatting

Existing anchor-based 3DGS compression methods primarily focus on spatial context construction, while the interaction between coding order and frequency statistics remains less explored. This ordering can affect probability estimation and, consequently, compression performance. In context-based entropy coding, each anchor estimates its probability based on the causal history. However, due to the coding order, the finite causal window may contain many statistically inconsistent anchors, which degrades rate estimation accuracy. We propose FDC-GS, a frequency-decoupled compression framework that organizes anchors into frequency-consistent levels. This organization provides a coding-level frequency state to the entropy model and reorganizes the composition of the decoded history, yielding more statistically relevant causal context and tighter probability estimates without redesigning the context backbone. FDC-GS first applies saliency-guided decomposition to separate a low-frequency base from informative high-frequency residuals, and then aligns these components with coarse and refinement levels for base-to-refinement coding. Finally, a dynamic R–D counterbalancing schedule increases the rate penalty as high-frequency supervision grows, controlling the bitrate growth induced by detail refinement. Based on widely used hash-grid and triplane contexts, FDC-GS achieves average BD-PSNR gains of 0.249 dB over HAC++ and 0.443 dB over TC-GS across four datasets.