Adaptive testing for video quality assessment

Optimizing the Quality of Experience and avoiding under or over provisioning in video delivery services requires understanding of how different resources affect the perceived quality. The utility of resources, such as bit-rate, is directly calculated by proportioningthe improvement in quality over the increase in costs. However, perception of quality in video is subjective and, hence, difficultand costly to directly estimate with the commonly used ratingmethods. Two-alternative-forced choice methods such asMaximum Likelihood Difference Scaling (MLDS) introduces less biases and variability, but only deliver estimates for relativedifference in quality rather than absolute rating. Nevertheless, thisinformation is sufficient for calculating the utility of the resourceon the video quality. In this work, we are presenting an adaptiveMLDS method, which incorporates an active test selectionscheme that improves the convergence rate and decreases theneed for executing the full range of tests.