Research output per year
Research output per year
Description
Deep learning approaches have had limited success on existing VQA datasets, either artificial or authentically distorted. We introduce KonViD-150k, an in-the-wild VQA dataset that is substantially larger and diverse, allowing the exploration of training DNNs on massive video collections with coarse annotations.
The database consists of two parts:
KonVid-150k-A: a coarsely annotated set of 152,265 videos, 5 seconds long, having five quality ratings each.
KonVid-150k-B: 1,577 videos with a minimum of 89 ratings each.
KonViD-150k provides a good testing ground for efficient VQA approaches, that are suitable to learn from large collections of videos, and can generalize well based on coarse annotations. Additionally, it is a great tool to investigate VQA methods with different annotation budget distribution strategies.
The database consists of two parts:
KonVid-150k-A: a coarsely annotated set of 152,265 videos, 5 seconds long, having five quality ratings each.
KonVid-150k-B: 1,577 videos with a minimum of 89 ratings each.
KonViD-150k provides a good testing ground for efficient VQA approaches, that are suitable to learn from large collections of videos, and can generalize well based on coarse annotations. Additionally, it is a great tool to investigate VQA methods with different annotation budget distribution strategies.
| Date made available | 2021 |
|---|---|
| Publisher | Universität Konstanz |
Research output
- 1 Article
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KonVid-150k: a dataset for no-reference video quality assessment of videos in-the-wild
Gotz-Hahn, F. (Lead / Corresponding author), Hosu, V., Lin, H. & Saupe, D., 2021, In: IEEE Access. 9, p. 72139-72160 22 p.Research output: Contribution to journal › Article › peer-review
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