Deep learning for quality assessment in live video streaming

M. Torres Vega, D.C. Mocanu, J. Famaey, S. Stavrou, A. Liotta

Research output: Contribution to journalArticleAcademicpeer-review

55 Citations (Scopus)
352 Downloads (Pure)


Video content providers put stringent requirements on the quality assessment methods realized on their services. They need to be accurate, real-time, adaptable to new content, and scalable as the video set grows. In this letter, we introduce a novel automated and computationally efficient video assessment method. It enables accurate real-time (online) analysis of delivered quality in an adaptable and scalable manner. Offline deep unsupervised learning processes are employed at the server side and inexpensive no-reference measurements at the client side. This provides both real-time assessment, as well as performance comparable to the full reference counter-part, while maintaining its no-reference characteristics. We tested our approach on the LIMP Video Quality Database (an extensive packet loss impaired video-set) obtaining a correlation between 78% and 91% to the FR benchmark (the Video Quality Metric, VQM). Due to its unsupervised learning essence, our method is flexible, dynamically adaptable to new content and scalable with the number of videos.
Original languageEnglish
Article number7892928
Pages (from-to)736 - 740
Number of pages5
JournalIEEE Signal Processing Letters
Issue number6
Publication statusPublished - Jun 2017


  • Deep learning (DL)
  • multimedia video services
  • unsupervised learning (UL)
  • video quality assessment


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