# Structured Image-based Coding for Efficient Gaussian Splatting Compression

arXiv: https://arxiv.org/abs/2601.14510 · HTML: https://mlsanity.com/paper/2601.14510/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2026.

## Built on by (most cited first, 3 of 3)

- [Only What Was Seen: Observation-Gram Compaction of View-Dependent Appearance in 3D Gaussian Splatting](https://mlsanity.com/paper/2609.28997/) (2026)
- [GS-PQM: A Parameter-Domain Quality Metric for Compressed Gaussian Splatting](https://mlsanity.com/paper/2610.00195/) (2026)
- [What Builds the Scene? Luminance Dominates Geometry Formation in 3D Gaussian Splatting](https://mlsanity.com/paper/2610.00749/) (2026)

## Abstract (arXiv)

Gaussian Splatting (GS) has recently emerged as a state-of-the-art representation for radiance fields, combining real-time rendering with high visual fidelity. However, GS models require storing millions of parameters, leading to large file sizes that impair their use in practical multimedia systems. To address this limitation, this paper introduces GS Image-based Compression (GSICO), a novel GS codec that efficiently compresses pre-trained GS models while preserving perceptual fidelity. The core contribution lies in a mapping procedure that arranges GS parameters into structured images, guided by a novel algorithm that enhances spatial coherence. These GS parameter images are then encoded using a conventional image codec. Experimental evaluations on Tanks and Temples, Deep Blending, and Mip-NeRF360 datasets show that GSICO achieves average compression factors of 20.2x with minimal loss in visual quality, as measured by PSNR, SSIM, and LPIPS. Compared with state-of-the-art GS compression methods, the proposed codec consistently yields superior rate-distortion (RD) trade-offs.