YouTube SFV+HDR Quality Dataset
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 8 Jun 2024.
Builds on
7 of 18 references.
References · 7
- 2022Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectivescited by 314
- 2022FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Samplingcited by 179
- 2020Patch-VQ: 'Patching Up' the Video Quality Problemcited by 147
- 2019YouTube UGC Dataset for Video Compression Researchcited by 124
- 2022Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessmentcited by 50
- 2021NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Resultscited by 36
- 2023AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detectioncited by 22
Built on by
Citing papers per year: 2024: 1; 2025: 3; 2026: 5.
9 papers.
- 2026NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Resultscited by 10
- 2025A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarkingcited by 8
- 2024An Ensemble Approach to Short-form Video Quality Assessment Using Multimodal LLMcited by 4
- 2025CHUG: Crowdsourced User-Generated HDR Video Quality Datasetcited by 3
- 2026Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcingcited by 2
- 2025Context and Pixel Aware Large Language Model for Video Quality Assessmentcited by 1
- 2026Seeing Beyond 8bits: Subjective and Objective Quality Assessment of HDR-UGC Videoscited by 1
- 2026MDS-VQA: Model-Informed Data Selection for Video Quality Assessmentcited by 1
- 2026FGSVQA: Frequency-Guided Short-form Video Quality Assessmentcited by 0
- 2026FGSVQA: Frequency-Guided Short-form Video Quality Assessment19 May 2026
- 2026NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results16 Apr 2026
- 2026MDS-VQA: Model-Informed Data Selection for Video Quality Assessment12 Mar 2026
- 2026Seeing Beyond 8bits: Subjective and Objective Quality Assessment of HDR-UGC Videos1 Mar 2026
- 2026Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcing26 Feb 2026
- 2025CHUG: Crowdsourced User-Generated HDR Video Quality Dataset10 Oct 2025
- 2025A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking26 May 2025
- 2025Context and Pixel Aware Large Language Model for Video Quality Assessment21 May 2025
- 2024An Ensemble Approach to Short-form Video Quality Assessment Using Multimodal LLM24 Dec 2024