Youtube SFV+HDR Quality Dataset

The popularity of Short form videos (SFV) has grown dramatically in the past few years, and has become a phenomenal video category with billions of viewers. Meanwhile, High Dynamic Range (HDR) as an advanced feature also becomes more and more popular on video sharing platforms. As a hot topic with huge impact, SFV and HDR bring new questions to video quality research: 1) is SFV+HDR quality assessment significantly different from traditional User Generated Content (UGC) quality assessment? 2) do objective quality metrics designed for traditional UGC still work well for SFV+HDR? To answer the above questions, we created the first large scale SFV+HDR dataset with reliable subjective quality scores, covering 10 popular content categories. Further, we also introduce a general sampling framework to maximize the representativeness of the dataset. We provided a comprehensive analysis of subjective quality scores for Short form SDR and HDR videos, and discuss the reliability of state-of-the-art UGC quality metrics and potential improvements.

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Publication Details

Published
2024-09-27
DOI
https://doi.org/10.1109/icip51287.2024.10647927
Citations
3
Primary Topic
Image Enhancement Techniques
Type
article
Field-Weighted Citation Impact
0.75
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article

Youtube SFV+HDR Quality Dataset

Balu Adsumilli, Joong Gon Yim, Neil Birkbeck, Yilin Wang
3 citations
Image Enhancement Techniques
0.75
article

Youtube SFV+HDR Quality Dataset

Balu Adsumilli, Joong Gon Yim, Neil Birkbeck, Yilin Wang
article en
3 citations

Abstract

The popularity of Short form videos (SFV) has grown dramatically in the past few years, and has become a phenomenal video category with billions of viewers. Meanwhile, High Dynamic Range (HDR) as an advanced feature also becomes more and more popular on video sharing platforms. As a hot topic with huge impact, SFV and HDR bring new questions to video quality research: 1) is SFV+HDR quality assessment significantly different from traditional User Generated Content (UGC) quality assessment? 2) do objective quality metrics designed for traditional UGC still work well for SFV+HDR? To answer the above questions, we created the first large scale SFV+HDR dataset with reliable subjective quality scores, covering 10 popular content categories. Further, we also introduce a general sampling framework to maximize the representativeness of the dataset. We provided a comprehensive analysis of subjective quality scores for Short form SDR and HDR videos, and discuss the reliability of state-of-the-art UGC quality metrics and potential improvements.

Google (United States) (US)
Openalex Percentile: Top 27%
Image Enhancement Techniques
0.75
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