针对渲染新缺陷，构建了首个合成视频质量数据集与评估模型。
CGVQM+D: Computer Graphics Video Quality Metric and Dataset
- 基于先进渲染技术生成带噪视频，构建新数据集
- 新指标在合成内容上相关性达0.78以上，显著优于旧方法
- 适合渲染算法开发、质量评估研究者使用
现有图像与视频质量数据集多聚焦自然内容和传统失真，对合成内容及现代渲染伪影的感知研究仍不充分。本文提出一个面向先进渲染技术引入失真的新型视频质量数据集，涵盖神经超采样、新视角合成、路径追踪、神经去噪、帧插值和可变帧率着色等技术。评估显示，现有全参考质量度量在这些失真上表现不佳，最大皮尔逊相关性仅为0.78。此外，我们发现预训练3D CNN的特征空间与人类视觉质量感知高度一致。为此，提出CGVQM全参考视频质量度量，在生成全局评分的同时输出逐像素误差图，显著超越现有方法。数据集与代码已开源：https://github.com/IntelLabs/CGVQM。
原文摘要 · Abstract (English)
While existing video and image quality datasets have extensively studied natural videos and traditional distortions, the perception of synthetic content and modern rendering artifacts remains underexplored. We present a novel video quality dataset focused on distortions introduced by advanced rendering techniques, including neural supersampling, novel-view synthesis, path tracing, neural denoising, frame interpolation, and variable rate shading. Our evaluations show that existing full-reference quality metrics perform sub-optimally on these distortions, with a maximum Pearson correlation of 0.78. Additionally, we find that the feature space of pre-trained 3D CNNs aligns strongly with human perception of visual quality. We propose CGVQM, a full-reference video quality metric that significantly outperforms existing metrics while generating both per-pixel error maps and global quality scores. Our dataset and metric implementation is available at https://github.com/IntelLabs/CGVQM.
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