The Frankenstone toolbox for video quality analysis of user-generated content

User-generated video content is one major part of currently streamed video content. Providers such as YouTube, Twitch, or Vimeo provide thousands of videos to users. However, the quality of user-generated content can vary widely, if not only purely technical, quality-related aspects are considered, but also the liking of the content is taken into account. Several studies and published open-source models aim to predict the quality scores of user-generated content. We propose in this paper a unified toolbox – Frankenstone – that includes the latest video quality prediction models for user-generated content. As well as recently published models also meta-data and signal-based features are included in the toolbox. The Frankenstone toolbox relies on the usage of GPUs for the calculation. We evaluate our toolbox with the test data of the YouTube UGC Dataset.