Content-independent and loss-pattern-aware distortion evaluation for streaming media

It is well known that dispersed and burst packet losses introduce significantly different amount of distortions. Since perceptual models are typically content dependent, it is challenging to characterize how losses interact with concealment. This paper presents loss-pattern-aware distortion (LoPAD), a content-independent metric that explicitly models the impact of different loss patterns. LoPAD operates solely on the loss trace without analyzing received media. It is fast, and supports offline and cloud-based monitoring of network impairment. Taking audio conferencing as target application, we show that full-reference PESQ scores for a collection of speech samples can be closely approximated by LoPAD. For various combinations of erasure channel models and forward error correction (FEC) codes, the correlation coefficients between LoPAD and PESQ-DMOS range from 0.90 to 0.97.