LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts
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Abstract (arXiv)
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/
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 47 references that are in the map. First posted on arXiv 5 Jul 2025.
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19 of 47 references.
References · 19
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- 2022Kernel Neural Optimal Transportcited by 31
- 2022Video compression dataset and benchmark of learning-based video-quality metricscited by 27
- 2020Subjective and Objective Quality Assessment of High Frame Rate Videoscited by 24
- 2023Measuring the Quality of Text-to-Video Model Outputs: Metrics and Datasetcited by 23
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