CGVQM+D: Computer Graphics Video Quality Metric and Dataset
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913891648274432 |
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| author | Jindal, Akshay Sadaka, Nabil Thomas, Manu Mathew Sochenov, Anton Kaplanyan, Anton |
| author_facet | Jindal, Akshay Sadaka, Nabil Thomas, Manu Mathew Sochenov, Anton Kaplanyan, Anton |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11546 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CGVQM+D: Computer Graphics Video Quality Metric and Dataset Jindal, Akshay Sadaka, Nabil Thomas, Manu Mathew Sochenov, Anton Kaplanyan, Anton Graphics Computer Vision and Pattern Recognition 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. |
| title | CGVQM+D: Computer Graphics Video Quality Metric and Dataset |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.11546 |