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UGC-VIDEO: perceptual quality assessment of user-generated videos

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arxiv 1908.11517 v2 pith:ITIWY3QE submitted 2019-08-30 cs.MM eess.IV

Yang Li , Shengbin Meng , Xinfeng Zhang , Shiqi Wang , Yue Wang , Siwei Ma This is my paper · ORCID
classification cs.MMeess.IV
keywords qualityvideoassessmentvideosperceptualcompressioncontentdatabase
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent years have witnessed an ever-expandingvolume of user-generated content (UGC) videos available on the Internet. Nevertheless, progress on perceptual quality assessmentof UGC videos still remains quite limited. There are many distinguished characteristics of UGC videos in the complete video production and delivery chain, and one important property closely relevant to video quality is that there does not exist the pristine source after they are uploaded to the hosting platform,such that they often undergo multiple compression stages before ultimately viewed. To facilitate the UGC video quality assessment,we created a UGC video perceptual quality assessment database. It contains 50 source videos collected from TikTok with diverse content, along with multiple distortion versions generated bythe compression with different quantization levels and coding standards. Subjective quality assessment was conducted to evaluate the video quality. Furthermore, we benchmark the database using existing quality assessment algorithms, and potential roomis observed to future improve the accuracy of UGC video quality measures.

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  1. LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts

    cs.CV 2025-07 conditional novelty 6.0 of 10

    LEHA-CVQAD is a 6,240-clip compressed video dataset with fused MOS and pairwise labels, a hidden test set, and a new Rate-Distortion Alignment Error metric.

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