构建大规模视频压缩质量评估数据集，支持模型性能精准评测
LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts
- 构建6240段视频的压缩质量数据集，覆盖59源视频与186种编码配置
- 提出新指标RDAE，发现主流VQA模型在码率-质量排序上误差显著
- 数据集含盲测部分，适合视频编解码优化与VQA模型验证
本文提出LEHA-CVQAD（大规模丰富人工标注压缩视频质量评估）数据集，包含6,240段视频片段，用于面向压缩的视频质量评估。59个源视频经186种编解码器-预设组合编码，生成180万对比较样本和1,500个主观评分（MOS），融合为单一质量尺度；部分视频保留用于盲测评估。我们还提出速率-失真对齐误差（RDAE）这一新评价指标，量化VQA模型在保持码率-质量排序一致性方面的表现，直接支持编解码参数调优。测试结果显示，主流IQA/VQA方法存在高RDAE值和低相关性，凸显该数据集的挑战性与实用性。公开数据及结果见https://aleksandrgushchin.github.io/lcvqad/
原文摘要 · Abstract (English)
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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