# YouTube SFV+HDR Quality Dataset

arXiv: https://arxiv.org/abs/2406.05305 · HTML: https://mlsanity.com/paper/2406.05305/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 8 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives](https://mlsanity.com/paper/2211.04894/) (2022)
- [FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling](https://mlsanity.com/paper/2207.02595/) (2022)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [YouTube UGC Dataset for Video Compression Research](https://mlsanity.com/paper/1904.06457/) (2019)
- [Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment](https://mlsanity.com/paper/2210.05357/) (2022)
- [NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results](https://mlsanity.com/paper/2106.01439/) (2021)
- [AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection](https://mlsanity.com/paper/2304.06116/) (2023)

## Built on by (most cited first, 9 of 9)

- [NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results](https://mlsanity.com/paper/2604.14816/) (2026)
- [A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking](https://mlsanity.com/paper/2505.19858/) (2025)
- [An Ensemble Approach to Short-form Video Quality Assessment Using Multimodal LLM](https://mlsanity.com/paper/2412.18060/) (2024)
- [CHUG: Crowdsourced User-Generated HDR Video Quality Dataset](https://mlsanity.com/paper/2510.09879/) (2025)
- [Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcing](https://mlsanity.com/paper/2602.22659/) (2026)
- [Context and Pixel Aware Large Language Model for Video Quality Assessment](https://mlsanity.com/paper/2505.16025/) (2025)
- [Seeing Beyond 8bits: Subjective and Objective Quality Assessment of HDR-UGC Videos](https://mlsanity.com/paper/2603.00938/) (2026)
- [MDS-VQA: Model-Informed Data Selection for Video Quality Assessment](https://mlsanity.com/paper/2603.11525/) (2026)
- [FGSVQA: Frequency-Guided Short-form Video Quality Assessment](https://mlsanity.com/paper/2605.20016/) (2026)

## 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.