The Konstanz natural video database (KoNViD-1k)

By Vlad Hosu, Franz Hahn, Mohsen Jenadeleh, Hanhe Lin, H. Men, Tamás Szirányi, Shujun Li, Dietmar Saupe. International Workshop on Quality of Multimedia Experience, 2017

359 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ae95abd2406ddfab1aa2ffa2413d68b98b1ba21b

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In Computer Vision and Pattern Recognition

Citations in the corpus

Quality Assessment of In-the-Wild Videos (2019, methodology): “There are four databases constructed for our problem: LIVE Video Quality Challenge Database (LIVE-VQC) [42], Konstanz Natural Video Database (KoNViD-1k) [12], LIVE-Qualcomm Mobile In-Capture Video Quality Database (LIVE-Qualcomm) [10], and Camera Video Database (CVD2014) [31].”
FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling (2022, methodology): “In recent years, deep-learning-based VQA methods [22,23,40,8,42,21] have shown better performance on in-the-wild VQA benchmarks [32,12,38,40].”
Two-Level Approach for No-Reference Consumer Video Quality Assessment (2019, methodology): “based study conducted in a specific fixed location [14], [26].”
KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment (2019, methodology): “In comparison to lab-based IQA studies that are limited in size, crowdsourcing has been successfully applied for larger databases of images [23] and videos [27] (to about 1,000 stimuli).”
Large-Scale Study of Perceptual Video Quality (2018, background): “A signiﬁcant and recent crowdsourced VQA database was reported in [21], providing an important new resource to the video quality community.”
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2020, methodology): “[10] by leveraging a bag of NSS features drawn from diverse”
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives (2022, methodology): “Similar to existing studies [1, 3], we would like the sampled video database able to represent the overall quality of the original larger database.”
UVG dataset: 50/120fps 4K sequences for video codec analysis and development (2020, methodology): “The dataset was evaluated across blur, colorfulness, contrast, spatial information, temporal information, and video quality [8].”