Large-Scale Study of Perceptual Video Quality

The great variations of videographic skills in videography, camera designs, compression and processing protocols, communication and bandwidth environments, and displays leads to an enormous variety of video impairments. Current no-reference (NR) video quality models are unable to handle this diversi...

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Vydáno v:IEEE transactions on image processing Ročník 28; číslo 2; s. 612 - 627
Hlavní autoři: Sinno, Zeina, Bovik, Alan Conrad
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.02.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Cameras
Computer simulation
crowd sourcing
Distortion
Downloading
Image coding
multimedia
Quality
Quality assessment
Streaming media
Uniqueness
User generated content
Video compression
Video data
Video quality assessment
Videography
ISSN:1057-7149, 1941-0042, 1941-0042
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Shrnutí:The great variations of videographic skills in videography, camera designs, compression and processing protocols, communication and bandwidth environments, and displays leads to an enormous variety of video impairments. Current no-reference (NR) video quality models are unable to handle this diversity of distortions. This is true in part because available video quality assessment databases contain very limited content, fixed resolutions, were captured using a small number of camera devices by a few videographers and have been subjected to a modest number of distortions. As such, these databases fail to adequately represent real world videos, which contain very different kinds of content obtained under highly diverse imaging conditions and are subject to authentic, complex, and often commingled distortions that are difficult or impossible to simulate. As a result, NR video quality predictors tested on real-world video data often perform poorly. Toward advancing NR video quality prediction, we have constructed a large-scale video quality assessment database containing 585 videos of unique content, captured by a large number of users, with wide ranges of levels of complex, authentic distortions. We collected a large number of subjective video quality scores via crowdsourcing. A total of 4776 unique participants took part in the study, yielding over 205 000 opinion scores, resulting in an average of 240 recorded human opinions per video. We demonstrate the value of the new resource, which we call the live video quality challenge database (LIVE-VQC), by conducting a comparison with leading NR video quality predictors on it. This paper is the largest video quality assessment study ever conducted along several key dimensions: number of unique contents, capture devices, distortion types and combinations of distortions, study participants, and recorded subjective scores. The database is available for download on this link: http://live.ece.utexas.edu/research/LIVEVQC/index.html.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2018.2869673