# YouTube UGC Dataset for Video Compression Research

## TL;DR

This paper introduces a large-scale YouTube UGC dataset for video compression research, highlighting the challenges of traditional metrics on non-pristine content and proposing no-reference quality assessment methods.

## Contribution

The paper provides a new extensive UGC dataset with diverse categories and features, and discusses novel challenges and solutions for compression and quality evaluation of UGC.

## Key findings

- Traditional metrics are less effective on UGC.
- No-reference quality metrics show promise for UGC assessment.
- The dataset covers popular categories like Gaming, Sports, HDR.

## Abstract

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).