构建首个针对压缩高斯点云的主观质量评估数据集
GScomp-QA: A Subjective Dataset for Quality Assessment of Compressed Gaussian Splatting
- 采集13个真实场景的331段视频，对比9种压缩方案
- 20人主观评测显示现有客观指标无法完全捕捉压缩失真
- 适合研究3D重建压缩与感知质量评估的学者使用
高斯点云（Gaussian Splatting, GS）已成为高质量三维重建与新视角合成的有效表示方法，但其庞大的模型尺寸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。为此，本文提出GScomp-QA，一个用于评估压缩后GS模型合成质量的主观质量评估数据集。该数据集包含13个真实场景的331段视频，覆盖9种先进的GS压缩方法。通过以未压缩模型生成的视频作为参考，GScomp-QA有效分离了压缩引起的失真与合成伪影。基于20名参与者开展的主观实验，获得了可靠的感知评分，并据此进行感知率-失真分析。同时评估了18种客观质量度量，发现它们未能充分捕捉GS特有的失真。GScomp-QA将公开发布，为评估GS压缩方案及开发适配的度量标准提供基准。
原文摘要 · Abstract (English)
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.
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