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）作为辐射场表示的最新前沿技术，结合了实时渲染与高视觉保真度。然而，GS模型需要存储数百万参数，导致文件体积庞大，限制了其在实用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）——一种创新的GS编解码器，能够在保持感知保真度的同时高效压缩预训练的GS模型。其核心贡献在于提出一种映射流程，该流程通过增强空间一致性的新算法引导，将GS参数组织为结构化图像。随后使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及Mip-NeRF360数据集上的实验评估表明，GSICO在PSNR、SSIM和LPIPS指标下，能以20.2倍的平均压缩率实现视觉质量的最小损失。与当前最先进的GS压缩方法相比，该编解码器在率失真（RD）权衡方面始终展现出更优性能。