Efficient measurement of quality at scale in Facebook video ecosystem
Shankar L. Regunathan, Haixiong Wang, Yun Zhang, Yu Liu, David Wolstencroft, Srinath Reddy, Cosmin Stejerean, Sonal G. Gandhi, Minchuan Chen, Pankaj Sethi, Amit Puntambekar, M. P. Coward, Ioannis X. Katsavounidis · 2020
This paper describes FB-MOS metric that measures video quality at scale in Facebook ecosystem. As the quality of uploaded UGC source itself varies widely, FB-MOS consists of both a no-reference component to assess input (upload) quality and a full-reference component, based on SSIM, to assess quality preserved in the transcoding and delivery pipeline. Note that the same video may be watched on a variety of devices (Mobile/laptop/TV) in varying network conditions that cause quality fluctuations; moreover, the viewer can switch between in-line view and full-screen view during the same viewing session. We show how FB-MOS metric accounts for all this variation in viewing condition while minimizing the computation overhead. Validation of this metric on FB-content has shown that SROCC is 0.9147 using internally selected videos. The paper also discusses some of the optimizations to reduce metric computation complexity and scale the complexity in proportion to video popularity.