Evaluation of Video Quality Monitoring Based on Pre-computed Frame Distortions

Abstract

Abstract.A large fraction of the current Internet traffic is caused by video streaming. Due to the growing expectations of video consumers, monitoring video applications is getting more and more important for network and service providers. In a previous work, we proposed a video quality monitoring solution which utilizes the full reference SSIM metric to improve the monitoring in the network by distributing pre-computed distortion information induced by frame losses. To improve scalability, we introduced a less complex algorithm which infers the distortion for higher loss scenarios from single loss scenarios and inter-frame dependencies. In this work, we evaluate the accuracy of our algorithm by comparing it with the exact calculation of the SSIM metric for different frame loss scenarios. We further consider different high definition test video sequences and group of picture structures and investigate the influence on the accuracy of our proposed approximation.

Optimization of Controller Placement and Information Flow in Softwarized Networks

“…On the other hand, flows that have the same five-tuple are always assigned to the same path since the result of the hash function does not change. All described changes are performed in ONOS's ConncectivityIntentCompiler class and have been submitted to the official repository as a pull request 5 .…”