Large-Scale Study of Perceptual Video Quality

By Zeina Sinno, A. Bovik. IEEE Transactions on Image Processing, 2018

307 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7954410ea395fe0b74db571e4bf87cc5b312ca6b

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In Computer Vision and Pattern Recognition

Citations in the corpus

UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2020, methodology): “PUBLIC LARGE-SCALE USER-GENERATED CONTENT VIDEO QUALITY ASSESSMENT (UGC-VQA) DATABASES COMPARED: KONVID-1K [10], LIVE-VQC [9], AND YOUTUBE-UGC [11]”
Quality Assessment of In-the-Wild Videos (2019, methodology): “ferencevideosin this situation, only NR-VQA methods are applicable. Unfortunately, the evaluation of current state-of-the-art NR-VQA methods [28, 35] on these video databases shows a poor performance [10, 23, 31, 42]. Existing deep learning-based VQA models are unfeasible in our problemsincetheyeith”
Two-Level Approach for No-Reference Consumer Video Quality Assessment (2019, methodology): “The pioneering dataset with natural distortions was Camera Video Database (CVD2014) [13], followed by Konstanz Natural Video Database (KoNViD-1k) [14], LIVE-Qualcomm [15], and LIVE Video Quality Challenge (LIVE-VQC) [16] databases.”
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality (2019, methodology): “Subject rejection: We took the recommended steps [5, 63] to ensure the quality of the collected human data.”