LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts
This provides a dataset and metric for video quality assessment, aiding in codec parameter tuning, but it is incremental as it builds on existing VQA methods.
The authors tackled the problem of assessing video quality under compression artifacts by introducing the LEHA-CVQAD dataset with 6,240 clips and 1.5k MOS ratings, and found that popular VQA metrics show high RDAE and lower correlations.
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/