LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts

• URL: http://arxiv.org/abs/2507.03990v2
• Date: Tue, 08 Jul 2025 11:26:39 GMT
• Title: LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts
• Authors: Aleksandr Gushchin, Maksim Smirnov, Dmitriy Vatolin, Anastasia Antsiferova,
• Abstract summary: We propose the LEHA-QAD dataset, which comprises 6,240 clips for compression-oriented video quality assessment.<n>59 source videos are encoded with 186-preset variants, 1.8M pairwise, and 1.5k ratings are fused into a single quality scale.<n>We also propose Rate-Distortion Alignment Error (RDAE), a novel evaluation metric that quantifies how well VQA models preserve-quality ordering.
• Score: 44.95552843771737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/

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