Measuring Video Quality Using AI: Why It’s Relevant and Reliable
Assuring video quality across mobile networks is complicated, with a myriad of dynamic challenges. These challenges can be overcome through comprehensive testing, but there are several different ways to measure video performance. Rather than analyzing packets or frames for diagnostic testing, many performance evaluation methods use pixel comparisons of the source versus the delivered video to determine overall quality. This is a common standardized method, but unfortunately it is not applicable to most Over-the-Top (OTT) streaming applications. Now there’s something new.
This white paper discusses Spirent’s unique approach to video performance analysis using machine-learning-based algorithms that closely match what a person “sees,” as if thousands of humans were scoring each video. The paper focuses specifically on Umetrix Video’s Non-Reference for Compression model – how it works and achieves a >90% correlation to VMAF, the state-of-the-art perceptual video quality metric – and why that matters.