AI

Video Quality Assessment for Web Content Mirroring

Abstract

Due to the increasing user expectation on watching experience, moving web high quality video streaming content from the small screen in mobile devices to the larger TV screen has become popular. It is crucial to develop video quality metrics to measure the quality change for various devices or network conditions. In this paper, we propose an automated scoring system to quantify user satisfaction. We compare the quality of local videos with the videos transmitted to a TV. Four video quality metrics, namely Image Quality, Rendering Quality, Freeze Time Ratio and Rate of Freeze Events are used to measure video quality change during web content mirroring. To measure image quality and rendering quality, we compare the matched frames between the source video and the destination video using barcode tools. Freeze time ratio and rate of freeze events are measured after extracting video timestamps. Several user studies are conducted to evaluate the impact of each objective video quality metric on the subjective user watching experience.