| This thesis introduces a new method to measure subjective ratings of technical quality (TQ) in disrupted video (DV). Current measurement methods are subject to extraneous factors such as delays introduced by the streaming software/hardware and the impact of video content. The proposed method is designed to supplement current methods by avoiding the confounding effects of content on TQ ratings and by reducing the time and effort necessary to run experiments. The new method replaces actual video with images representative of videos that contain various disruptions. In the research reported in this thesis, I ran an experiment with 37 participants to explore the viability of assessing TQ using visualizations instead of actual videos. The experiment compared ratings made after actually watching videos that varied in TQ versus ratings made after viewing the TQ visualizations, without actually seeing corresponding videos with associated impairments or failures. The results of the experiment showed that, with appropriate training, ratings of TQ made, after viewing the visualizations only, were similar to TQ ratings made after actually watching videos with corresponding impairments or failures. |
