The Konstanz natural video database (KoNViD-1k)
| Autor: | Hui Men, Shujun Li, Hanhe Lin, Dietmar Saupe, Tamás Szirányi, Vlad Hosu, Franz Hahn, Mohsen Jenadeleh |
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| Rok vydání: | 2017 |
| Předmět: |
Database
Computer science business.industry Deep learning Video database authentic video video qualitiy assessment fair samling crowdsourcing 020206 networking & telecommunications Context (language use) 02 engineering and technology Semantics Video quality computer.software_genre Crowdsourcing QA76.575 TK7885 0202 electrical engineering electronic engineering information engineering Natural (music) 020201 artificial intelligence & image processing Artificial intelligence ddc:004 PEVQ business computer Subjective video quality |
| Zdroj: | QoMEX |
| ISSN: | 2472-7814 |
| DOI: | 10.1109/qomex.2017.7965673 |
| Popis: | Subjective video quality assessment (VQA) strongly depends on semantics, context, and the types of visual distortions. Currently, all existing VQA databases include only a small num- ber of video sequences with artificial distortions. The development and evaluation of objective quality assessment methods would benefit from having larger datasets of real-world video sequences with corresponding subjective mean opinion scores (MOS), in particular for deep learning purposes. In addition, the training and validation of any VQA method intended to be ‘general purpose’ requires a large dataset of video sequences that are representative of the whole spectrum of available video content and all types of distortions. We report our work on KoNViD-1k, a subjectively annotated VQA database consisting of 1,200 public- domain video sequences, fairly sampled from a large public video dataset, YFCC100m. We present the challenges and choices we have made in creating such a database aimed at ‘in the wild’ authentic distortions, depicting a wide variety of content. published |
| Databáze: | OpenAIRE |
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