高斯溅射压缩与质量评估研究基准
A Benchmark for Gaussian Splatting Compression and Quality Assessment Study
AI总结：
提出基于图的GS压缩方法GGSC及含120样本的GSQA数据集，通过主观实验分析不同属性失真对视觉质量的影响，并公开代码与数据。
摘要：
为填补传统高斯溅射（GS）压缩方法的空白，本文首先提出一种简单有效的GS数据压缩锚点方法，称为基于图的高斯溅射压缩（GGSC）。GGSC受图信号处理理论启发，采用两个分支分别压缩基元中心和属性。我们通过KDTree分割整个GS样本，并在图傅里叶变换后裁剪高频分量。随后进行量化，使用G-PCC和自适应算术编码压缩基元中心和属性残差矩阵以生成比特率文件。GGSC是首个探索传统GS压缩的工作，其优势在于能够揭示典型压缩操作（如高频裁剪和量化）对应的GS失真特性。其次，基于GGSC，我们创建了一个包含120个样本的GS质量评估数据集（GSQA）。在实验室环境中进行主观实验，将GS渲染为处理视频序列（PVS）后收集主观评分。我们基于平均意见分数（MOS）分析不同GS失真的特性，展示不同属性失真对视觉质量的敏感性。GGSC代码及数据集（包括GS样本、MOS和PVS）已在https://github.com/Qi-Yangsjtu/GGSC公开提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
