Predictive no-reference assessment of video quality

Torres Vega, Maria; Mocanu, Decebal Constantin; Stavrou, Stavros; Liotta, Antonio

Abstract

Among the various means to evaluate the quality of video streams, lightweight No-Reference (NR) methods have low computation and may be executed on thin clients. Thus, these methods would be perfect candidates in cases of real-time quality assessment, automated quality control and in adaptive mobile streaming. Yet, existing real-time, NR approaches are not typically designed to tackle network distorted streams, thus performing poorly when compared to Full-Reference (FR) algorithms. In this work, we present a generic NR method whereby machine learning (ML) may be used to construct a quality metric trained on simplistic NR metrics. Testing our method on nine, representative ML algorithms allows us to show the generality of our approach, whilst finding the best-performing algorithms. We use an extensive video dataset (960 video samples), generated under a variety of lossy network conditions, thus verifying that our NR metric remains accurate under realistic streaming scenarios. In this way, we achieve a quality index that is comparably as computationally efficient as typical NR metrics and as accurate as the FR algorithm Video Quality Metric (97% correlation).

• Journal Article Type: Article
• Acceptance Date: Dec 4, 2016
• Online Publication Date: Dec 8, 2016
• Publication Date: 2017-03
• Deposit Date: Aug 2, 2019
• Publicly Available Date: Aug 2, 2019
• Journal: Signal Processing: Image Communication
• Print ISSN: 0923-5965
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 52
• Pages: 20-32
• DOI: https://doi.org/10.1016/j.image.2016.12.001
• Keywords: Quality of experience, No-Reference Video quality assessment, Supervised machine learning
• Public URL: http://researchrepository.napier.ac.uk/Output/2022668
• Contract Date: Aug 2, 2019