To fill the gap of traditional GS compression method, in this paper, we first propose a simple and effective GS data compression anchor called Graph-based GS Compression (GGSC). GGSC is inspired by graph signal processing theory and uses two branches to compress the primitive center and attributes. We split the whole GS sample via KDTree and clip the high-frequency components after the graph Fourier transform. Followed by quantization, G-PCC and adaptive arithmetic coding are used to compress the primitive center and attribute residual matrix to generate the bitrate file. GGSS is the first work to explore traditional GS compression, with advantages that can reveal the GS distortion characteristics corresponding to typical compression operation, such as high-frequency clipping and quantization. Second, based on GGSC, we create a GS Quality Assessment dataset (GSQA) with 120 samples. A subjective experiment is conducted in a laboratory environment to collect subjective scores after rendering GS into Processed Video Sequences (PVS). We analyze the characteristics of different GS distortions based on Mean Opinion Scores (MOS), demonstrating the sensitivity of different attributes distortion to visual quality. The GGSC code and the dataset, including GS samples, MOS, and PVS, are made publicly available at https://github.com/Qi-Yangsjtu/GGSC.

翻译：为填补传统高斯溅射压缩方法的空白，本文首先提出了一种简单有效的高斯溅射数据压缩基准方法——基于图的高斯溅射压缩。该方法受图信号处理理论启发，采用双分支结构分别压缩基元中心与属性。我们通过KDTree对完整高斯溅射样本进行分割，并在图傅里叶变换后截断高频分量。随后经过量化处理，采用G-PCC与自适应算术编码对基元中心及属性残差矩阵进行压缩以生成码流文件。该研究是探索传统高斯溅射压缩的首项工作，其优势在于能够揭示典型压缩操作（如高频截断与量化）对应的高斯溅射失真特性。其次，基于该压缩方法，我们构建了包含120个样本的高斯溅射质量评估数据集。通过在实验室环境下开展主观实验，将高斯溅射渲染为处理后视频序列后收集主观评分。基于平均意见得分，我们分析了不同高斯溅射失真的特性，论证了各类属性失真对视觉质量的敏感度差异。该压缩方法的代码及数据集（包括高斯溅射样本、平均意见得分与处理后视频序列）已公开于https://github.com/Qi-Yangsjtu/GGSC。