YouTube SFV+HDR Quality Dataset
This addresses the need for reliable quality assessment tools for the growing category of SFV+HDR videos on platforms like YouTube, which is incremental as it extends existing UGC quality research to a new data type.
The researchers tackled the problem of assessing video quality for Short Form Videos (SFV) with High Dynamic Range (HDR) by creating the first large-scale SFV+HDR dataset with subjective quality scores, covering 10 content categories, and analyzed how existing quality metrics perform on this new data.
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.