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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2408.07171 (eess)
[Submitted on 13 Aug 2024]

Title:BVI-UGC: A Video Quality Database for User-Generated Content Transcoding

Authors:Zihao Qi, Chen Feng, Fan Zhang, Xiaozhong Xu, Shan Liu, David Bull
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Abstract:In recent years, user-generated content (UGC) has become one of the major video types consumed via streaming networks. Numerous research contributions have focused on assessing its visual quality through subjective tests and objective modeling. In most cases, objective assessments are based on a no-reference scenario, where the corresponding reference content is assumed not to be available. However, full-reference video quality assessment is also important for UGC in the delivery pipeline, particularly associated with the video transcoding process. In this context, we present a new UGC video quality database, BVI-UGC, for user-generated content transcoding, which contains 60 (non-pristine) reference videos and 1,080 test sequences. In this work, we simulated the creation of non-pristine reference sequences (with a wide range of compression distortions), typical of content uploaded to UGC platforms for transcoding. A comprehensive crowdsourced subjective study was then conducted involving more than 3,500 human participants. Based on this collected subjective data, we benchmarked the performance of 10 full-reference and 11 no-reference quality metrics. Our results demonstrate the poor performance (SROCC values are lower than 0.6) of these metrics in predicting the perceptual quality of UGC in two different scenarios (with or without a reference).
Comments: 12 pages, 11 figures
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2408.07171 [eess.IV]
  (or arXiv:2408.07171v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2408.07171

Submission history

From: Zihao Qi [view email]
[v1] Tue, 13 Aug 2024 19:30:12 UTC (10,063 KB)
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