LSVQ Videos: Largest Non-reference Video Quality Assessment Dataset

LSVQ is the largest dataset available for Non-reference Video Quality Assessment (NR-VQA), as stated in the description. This unofficial copy facilitates research after reports that original links are unavailable. The dataset was created by Ying et al. and published at CVPR in 2021.

Use Cases

• Training no-reference video quality assessment models based on the dataset's labeled quality scores.
• Benchmarking new NR-VQA algorithms against the established LSVQ dataset.
• Studying the relationship between video features and perceived quality based on the dataset's content.

Strengths

• Dataset is described as the largest available for Non-reference Video Quality Assessment.
• Associated with peer-reviewed research (CVPR 2021) and subsequent work (ECCV 2022, ICCV 2023).

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Original LSVQ dataset (Ying et al, CVPR, 2021).

Freshness

Last updated 2023-11-13 14:23:27; freshness should be verified.