RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content Article Swipe
Zhengzhong Tu
,
Xiangxu Yu
,
Yilin Wang
,
Neil Birkbeck
,
Balu Adsumilli
,
Alan C. Bovik
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/ojsp.2021.3090333
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/ojsp.2021.3090333
Blind or no-reference video quality assessment of user-generated content (UGC) has become a trending, challenging, heretofore unsolved problem. Accurate and efficient video quality predictors suitable for this content are thus in great demand to achieve more intelligent analysis and processing of UGC videos. Previous studies have shown that natural scene statistics and deep learning features are both sufficient to capture spatial distortions, which contribute to a significant aspect of UGC video quality issues. However, these models are either incapable or inefficient for predicting the quality of complex and diverse UGC videos in practical applications. Here we introduce an effective and efficient video quality model for UGC content, which we dub the Rapid and Accurate Video Quality Evaluator (RAPIQUE), which we show performs comparably to state-of-the-art (SOTA) models but with orders-of-magnitude faster runtime. RAPIQUE combines and leverages the advantages of both quality-aware scene statistics features and semantics-aware deep convolutional features, allowing us to design the first general and efficient spatial and temporal (space-time) bandpass statistics model for video quality modeling. Our experimental results on recent large-scale UGC video quality databases show that RAPIQUE delivers top performances on all the datasets at a considerably lower computational expense. We hope this work promotes and inspires further efforts towards practical modeling of video quality problems for potential real-time and low-latency applications. To promote public usage, an implementation of RAPIQUE has been made freely available online: \url{https://github.com/vztu/RAPIQUE}.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/ojsp.2021.3090333
- https://ieeexplore.ieee.org/ielx7/8782710/9336068/09463703.pdf
- OA Status
- gold
- Cited By
- 5
- References
- 103
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3125631097
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- OpenAlex ID
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https://openalex.org/W3125631097Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/ojsp.2021.3090333Digital Object Identifier
- Title
-
RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated ContentWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Zhengzhong Tu, Xiangxu Yu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, Alan C. BovikList of authors in order
- Landing page
-
https://doi.org/10.1109/ojsp.2021.3090333Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/8782710/9336068/09463703.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/8782710/9336068/09463703.pdfDirect OA link when available
- Concepts
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Computer science, Video quality, Quality (philosophy), Deep learning, Latency (audio), Video processing, Quality of experience, Artificial intelligence, Multimedia, Quality of service, Computer network, Telecommunications, Economics, Metric (unit), Operations management, Philosophy, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2022: 1, 2021: 2Per-year citation counts (last 5 years)
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103Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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