YouTube UGC Dataset for Video Compression Research

By Yilin Wang, Sasi Inguva, Balu Adsumilli. IEEE International Workshop on Multimedia Signal Processing, 2019

309 citations recorded by the source corpus.

Non-professional video, commonly known as User Generated Content (UGC) has become very popular in today's video sharing applications. However, traditional metrics used in compression and quality assessment, like BD-Rate and PSNR, are designed for pristine originals. Thus, their accuracy drops significantly when being applied on non-pristine originals (the majority of UGC). Understanding difficulties for compression and quality assessment in the scenario of UGC is important, but there are few public UGC datasets available for research. This paper introduces a large scale UGC dataset (1500 20 sec video clips) sampled from millions of YouTube videos. The dataset covers popular categories like Gaming, Sports, and new features like High Dynamic Range (HDR). Besides a novel sampling method based on features extracted from encoding, challenges for UGC compression and quality evaluation are also discussed. Shortcomings of traditional reference-based metrics on UGC are addressed. We demonstrate a promising way to evaluate UGC quality by no-reference objective quality metrics, and evaluate the current dataset with three no-reference metrics (Noise, Banding, and SLEEQ).

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/093561bf2c2112be497873d2e36dcc1648a035e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Ming-Hsuan Yang
3. Luc Van Gool
4. Weisi Lin
5. Lei Zhang
6. Chen Change Loy
7. Yi Yang
8. Xiaogang Wang

In Signal Processing

1. Jarno Vanne
2. Alexandre Mercat
3. Marko Viitanen
4. Weisi Lin
5. Oriol Vinyals
6. A. Bovik
7. Chenjie Gu
8. Chaofeng Chen

Citations in the corpus

• A Review of Safe Reinforcement Learning: Methods, Theories, and Applications (2024, unknown): “Their experiments proved that their method can achieve better performance than traditional methods and related modern machine learning methods on the YouTube UGC dataset [238].”
• A Review of Safe Reinforcement Learning: Methods, Theory and Applications (2022, methodology): “In their experiments, they have proven that their method can achieve better performance than traditional methods and related modern machine learning methods on the YouTube UGC dataset [290].”
• UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2020, methodology): “PUBLIC LARGE-SCALE USER-GENERATED CONTENT VIDEO QUALITY ASSESSMENT (UGC-VQA) DATABASES COMPARED: KONVID-1K [10], LIVE-VQC [9], AND YOUTUBE-UGC [11]”
• MuZero with Self-competition for Rate Control in VP9 Video Compression (2022, methodology): “• We evaluate this agent against libvpx’s two-pass VBR rate control implementation [WebM, 2017] on 3062 video clips, each of which are 5-seconds in length, obtained from the YouTube UGC dataset [Wang et al., 2019] which has been created for evaluating video compression algorithms.”
• Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives (2022, methodology): “Specifically, we evaluate on diverged pairs {V1,V2} where aesthetic branch predicts V1 is obviously better (at least one score higher when scores are in the range [1, 5]) yet technical branch predicts V2 is obviously better.”
• UVG dataset: 50/120fps 4K sequences for video codec analysis and development (2020, methodology): “Each video is available in YUV 4:2:0 format and in 360p, 480p, 720p, 1080p, and 2160p resolutions [12].”