# No-Reference Rendered Video Quality Assessment: Dataset and Metrics

arXiv: https://arxiv.org/abs/2510.13349 · HTML: https://mlsanity.com/paper/2510.13349/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives](https://mlsanity.com/paper/2211.04894/) (2022)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [A Deep Learning based No-reference Quality Assessment Model for UGC Videos](https://mlsanity.com/paper/2204.14047/) (2022)
- [Large-Scale Study of Perceptual Video Quality](https://mlsanity.com/paper/1803.01761/) (2018)
- [YouTube UGC Dataset for Video Compression Research](https://mlsanity.com/paper/1904.06457/) (2019)
- [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://mlsanity.com/paper/2011.04263/) (2020)
- [Dense Optical Tracking: Connecting the Dots](https://mlsanity.com/paper/2312.00786/) (2023)
- [Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment](https://mlsanity.com/paper/2012.13936/) (2020)
- [Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment](https://mlsanity.com/paper/2210.05357/) (2022)
- [MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos](https://mlsanity.com/paper/2303.14933/) (2023)
- [Modular Blind Video Quality Assessment](https://mlsanity.com/paper/2402.19276/) (2024)
- [Subjective and Objective Analysis of Streamed Gaming Videos](https://mlsanity.com/paper/2203.12824/) (2022)
- [Training and Predicting Visual Error for Real-Time Applications](https://mlsanity.com/paper/2310.09125/) (2023)

## Abstract (arXiv)

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.