  Among the various means to evaluate the quality of video streams,
No-Reference (NR) methods have low computation and may be executed on thin
clients. Thus, NR algorithms would be perfect candidates in cases of real-time
quality assessment, automated quality control and, particularly, in adaptive
mobile streaming. Yet, existing NR approaches are often inaccurate, in
comparison to Full-Reference (FR) algorithms, especially under lossy network
conditions. In this work, we present an NR method that combines machine
learning with simple NR metrics to achieve a quality index comparably as
accurate as the Video Quality Metric (VQM) Full-Reference algorithm. Our method
is tested in an extensive dataset (960 videos), under lossy network conditions
and considering nine different machine learning algorithms. Overall, we achieve
an over 97% correlation with VQM, while allowing real-time assessment of video
quality of experience in realistic streaming scenarios.
