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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.03990 |
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| _version_ | 1866911044293623808 |
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| author | Gushchin, Aleksandr Smirnov, Maksim Vatolin, Dmitriy Antsiferova, Anastasia |
| author_facet | Gushchin, Aleksandr Smirnov, Maksim Vatolin, Dmitriy Antsiferova, Anastasia |
| contents | We propose the LEHA-CVQAD (Large-scale Enriched Human-Annotated Compressed Video Quality Assessment) dataset, which comprises 6,240 clips for compression-oriented video quality assessment. 59 source videos are encoded with 186 codec-preset variants, 1.8M pairwise, and 1.5k MOS ratings are fused into a single quality scale; part of the videos remains hidden for blind evaluation. We also propose Rate-Distortion Alignment Error (RDAE), a novel evaluation metric that quantifies how well VQA models preserve bitrate-quality ordering, directly supporting codec parameter tuning. Testing IQA/VQA methods reveals that popular VQA metrics exhibit high RDAE and lower correlations, underscoring the dataset challenges and utility. The open part and the results of LEHA-CVQAD are available at https://aleksandrgushchin.github.io/lcvqad/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03990 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts Gushchin, Aleksandr Smirnov, Maksim Vatolin, Dmitriy Antsiferova, Anastasia Computer Vision and Pattern Recognition We propose the LEHA-CVQAD (Large-scale Enriched Human-Annotated Compressed Video Quality Assessment) dataset, which comprises 6,240 clips for compression-oriented video quality assessment. 59 source videos are encoded with 186 codec-preset variants, 1.8M pairwise, and 1.5k MOS ratings are fused into a single quality scale; part of the videos remains hidden for blind evaluation. We also propose Rate-Distortion Alignment Error (RDAE), a novel evaluation metric that quantifies how well VQA models preserve bitrate-quality ordering, directly supporting codec parameter tuning. Testing IQA/VQA methods reveals that popular VQA metrics exhibit high RDAE and lower correlations, underscoring the dataset challenges and utility. The open part and the results of LEHA-CVQAD are available at https://aleksandrgushchin.github.io/lcvqad/ |
| title | LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.03990 |