  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.
