Gaussian Splatting (GS) has emerged as an efficient representation for high-quality 3D reconstruction and novel view synthesis. However, its large model size poses challenges for storage and transmission. While several GS compression solutions have been proposed, their perceptual impact remains poorly understood due to the lack of dedicated evaluation datasets. To address this gap, this paper introduces GScomp-QA, a subjective quality assessment dataset for evaluating synthesis quality from compressed GS models. The dataset comprises 331 video stimuli from 13 real-world scenes, covering 9 state-of-the-art GS compression solutions. By using videos synthesized from uncompressed models as reference, GScomp-QA isolates compression-induced distortions from synthesis artifacts. A subjective study with 20 participants was conducted, providing reliable perceptual scores. Based on these data, GS compression solutions are evaluated through perceptual rate-distortion analysis. In addition, 18 objective quality metrics are evaluated, showing that they do not fully capture GS-specific distortions. GScomp-QA will be publicly available and provide a benchmark for evaluating GS compression solutions and supporting the development of quality metrics tailored to GS compression.

翻译：高斯泼溅（GS）已成为高质量三维重建与新视角合成的高效表示方法，但其模型体积庞大，给存储和传输带来挑战。尽管已有多种GS压缩方案被提出，但由于缺乏专用评估数据集，其感知影响仍不明确。为填补这一空白，本文提出了GScomp-QA，一个用于评估压缩GS模型合成质量的主观质量评估数据集。该数据集包含来自13个真实场景的331个视频刺激，涵盖9种最先进的GS压缩方案。通过以未压缩模型合成的视频为参考，GScomp-QA将压缩引起的失真与合成伪影分离。我们开展了20人参与的主观研究，获得了可靠的感知评分。基于这些数据，通过感知率失真分析评估了GS压缩方案。此外，我们还评估了18种客观质量指标，结果显示这些指标未能充分捕捉GS特有的失真。GScomp-QA将公开提供，为评估GS压缩方案和支持面向GS压缩的质量指标开发提供基准。