Predictive No-Reference Assessment of Video Quality
This work addresses the need for accurate real-time video quality assessment in adaptive mobile streaming, representing an incremental improvement over existing NR methods.
The paper tackled the problem of inaccurate No-Reference (NR) video quality assessment under lossy network conditions by combining machine learning with simple NR metrics, achieving over 97% correlation with the Full-Reference VQM algorithm on a dataset of 960 videos.
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.