AI Innovations in Testing and Monitoring: Transforming Video Quality Assurance on Physical Devices
Originally Aired - Monday, April 15 | 1:30 PM - 1:50 PM PT
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In today’s digital media landscape, audiences have come to expect nothing less than flawless streaming experiences. As a result, the highest priority for content and service providers is the delivery of pristine video quality. To meet their customers’ discerning expectations, it is imperative to conduct regular testing to assess and enhance the quality of video streams. Artificial intelligence has emerged as a powerful tool in delivering this capability. Currently, the predominant AI technology being employed for assessing video quality is machine learning neural networks. These neural networks play a pivotal role in identifying defects that emerge during the content compression process by measuring the degradation in quality between the source content and its compressed counterpart. This method, in its various iterations, is rapidly gaining traction among content providers. They have been harnessing AI tools to enhance their encoding processes, particularly for premium, high-demand content. While the use of neural networks in assessing video quality is helpful for content providers, it comes with a notable limitation for service providers — it’s unsuitable for any content that lacks a reference stream to compare against. Consequently, it cannot be employed for testing live content due to the inherent nature of live streams, which cannot be preprocessed. So, for service providers delivering live linear content like sporting events, the primary hurdle in harnessing AI lies in the ability to evaluate content quality in real time, including ad breaks. The primary topic of this panel will be a new innovative solution that involves evaluating video quality from the perspective of an end-user after the stream has been decoded by a viewing device. This approach requires a high level of AI sophistication, since it entails evaluating content without prior knowledge and assessing it in a manner akin to how a human would. It also mandates the utilization of real physical devices with distinct operating systems, which introduces its own set of complexities. Navigating the interface of physical devices necessitates an approach that mirrors human interaction. This is a capability best achieved through AI, as scripting every conceivable action to access every piece of content is unfeasible. Therefore, the process of content retrieval becomes dynamic, calling for adaptable AI solutions capable of handling diverse scenarios. Once the content is reached, a further challenge arises in distinguishing between genuine defects and non-defective scenarios. AI must differentiate between blurriness resulting from compression and deliberate artistic choices, or between a black screen indicative of a defect and one that occurs during a transition. The ability to accurately identify true defects is paramount to improve the provider’s service and enhance the user experience. AI is transforming the digital landscape in all aspects of media, and testing and monitoring video services is no different. If selected, this panel will cover recent AI-powered innovations in testing and monitoring on real devices, relating to everything from measuring video quality to scripting test scenarios. It will also address the role of machine learning in recent streaming trends, including FAST channels and dynamically inserted ads.
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