GScomp-QA: A Subjective Dataset for Quality Assessment of Compressed Gaussian Splatting
For researchers working on Gaussian Splatting compression, this dataset provides a much-needed benchmark to evaluate perceptual quality, addressing the lack of dedicated evaluation resources.
This paper introduces GScomp-QA, a subjective dataset for evaluating the perceptual quality of compressed Gaussian Splatting models, comprising 331 video stimuli from 13 scenes and 9 compression methods. A subjective study with 20 participants reveals that existing objective quality metrics fail to fully capture GS-specific distortions.
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.