No-Reference Rendered Video Quality Assessment: Dataset and Metrics

• URL: http://arxiv.org/abs/2510.13349v1
• Date: Wed, 15 Oct 2025 09:36:52 GMT
• Title: No-Reference Rendered Video Quality Assessment: Dataset and Metrics
• Authors: Sipeng Yang, Jiayu Ji, Qingchuan Zhu, Zhiyao Yang, Xiaogang Jin,
• Abstract summary: We present a large rendering-oriented video dataset with subjective quality annotations.<n>We calibrate our NR-VQA metric to assess rendered video quality by looking at both image quality and temporal stability.
• Score: 13.445406215772449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quality assessment of videos is crucial for many computer graphics applications, including video games, virtual reality, and augmented reality, where visual performance has a significant impact on user experience. When test videos cannot be perfectly aligned with references or when references are unavailable, the significance of no-reference video quality assessment (NR-VQA) methods is undeniable. However, existing NR-VQA datasets and metrics are primarily focused on camera-captured videos; applying them directly to rendered videos would result in biased predictions, as rendered videos are more prone to temporal artifacts. To address this, we present a large rendering-oriented video dataset with subjective quality annotations, as well as a designed NR-VQA metric specific to rendered videos. The proposed dataset includes a wide range of 3D scenes and rendering settings, with quality scores annotated for various display types to better reflect real-world application scenarios. Building on this dataset, we calibrate our NR-VQA metric to assess rendered video quality by looking at both image quality and temporal stability. We compare our metric to existing NR-VQA metrics, demonstrating its superior performance on rendered videos. Finally, we demonstrate that our metric can be used to benchmark supersampling methods and assess frame generation strategies in real-time rendering.

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