Steve Benting

Using video quality metrics for something other than compression

The development of video quality metrics and perceptual video quality metrics has been a well established pursuit for more than 25 years. The body of work has been seen to be most relevant for improving the performance of visual compression algorithms. However, modeling the human perception of video with an algorithm of some sort is notoriously complicated. As a result the perceptual coding of video remains challenging and no standards have incorporated perceptual video quality metrics within their specification. In this paper we present the use of video metrics at the system level of a video processing pipeline. We show that it is possible to combine the artefact detection and correction process by posing the problem as a classification exercise. We also present the use of video metrics as part of a classical testing pipeline for software infrastructure, but here it is sensitive to the perceived quality in picture degradation.

Geo-popularity assisted optimized transcoding for large scale adaptive streaming

HTTP-based video streaming techniques have now been widely deployed to deliver video streams over communication networks. With these techniques, a video player can dynamically select a video stream from a set of pre-encoded representations of the video source based on its available bandwidth and viewport size. The bitrates of the encoded representations thus determine the video quality presented to viewers and also the averaged streaming bitrate which is highly related to streaming cost for massive video streaming platforms. Our work minimizes the average streaming bitrate on a per-chunk basis by modeling the probability that a player observes a particular representation. Since popularity of videos is regional, this paper exploits a further optimization that uses regional statistics of client bandwidth and viewport instead of the global statistics. Simulation results demonstrate that using regional statistics reduces streaming cost for low-bandwidth regions while improving the delivered quality for high-bandwidth regions compared to a baseline configuration that uses global statistics.