Automated Quality Curation of Video Collections
T. Goodall, M. Esteva, S. Sweat, and A. C. Bovik
To assess how well large collections of videos are handled by latest I/VQA algorithms, we first conducted a small-scale study using video clips. Each video clips was extracted from different video sources. The quality of the source videos ranged from pristine to distorted with any number of unknown distortions.
These clips were scored by human subjects to compute Mean Opinion Scores (MOS). As Fig. 1 shows, the distribution of these MOS scores indicates a higher frequency of both bad and good quality videos. After inspection, this is representative of our larger video corpus.
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| Figure 1: Distribution of MOS scores. |
Unfortunately, we cannot release the video clips due to content license restrictions. Instead, we release the features computed on these video clips for BRISQUE and Video BLIINDS along with MOS scores gathered from the associated subjective test. This will allow video researchers to train their own models and compare against other datasets.
NOTE: before testing with your own collection, all input videos MUST be scaled to the Nexus 9 tablet resolution (2048x1536), while maintaining video aspect ratio. We chose bilinear upscaling for extracting the feature sets.
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The following papers are to be cited in the bibliography whenever the data is used as:
- T. Goodall, M. Esteva, S. Sweat, and A. C. Bovik. "Towards Automated Quality Curation of Video Collections from a Realistic Perspective," Joint Conference on Digital Libraries (JCDL), submitted Feb 2017
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The data can be obtained using the following links:
- Raw extracted features on video dataset: BRISQUE, Video BLIINDS
- MOS gathered on video dataset: scores