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.

翻译：高斯溅射（Gaussian Splatting，GS）近年来已成为辐射场表示的前沿技术，兼具实时渲染能力与高视觉保真度。然而，GS模型需要存储数百万参数，导致文件体积庞大，限制了其在实际多媒体系统中的应用。为突破此局限，本文提出GS图像压缩（GSICO）——一种新型GS编解码器，可在保持感知保真度的前提下高效压缩预训练的GS模型。其核心贡献在于提出一种映射流程：通过增强空间连贯性的新型算法引导，将GS参数重组为结构化图像。随后采用传统图像编解码器对这些GS参数图像进行编码。在Tanks and Temples、Deep Blending和Mip-NeRF360数据集上的实验评估表明，以PSNR、SSIM和LPIPS指标衡量，GSICO在视觉质量损失极小的前提下实现了平均20.2倍的压缩比。与当前最先进的GS压缩方法相比，该编解码器在率失真（RD）权衡方面始终表现出更优性能。