Leveraging AI for Media Creation

Leveraging AI for Media Creation

The following excerpts are from the TV Tech article “AI Carves an Easier Path for Media Creators“.

Like any other technology, artificial intelligence and machine learning for video production and distribution came about in an effort to build a better mousetrap. Any product that does more with less effort has an advantage over products that don’t make the leap to the latest technology—that’s why self-driving car technology is often in the news.

There is no self-driving product for the television industry… no editing device that will automatically assemble a program, no camera that will point and adjust itself perfectly without human intervention, and no transmitter or distribution chain that will perfectly adjust itself to changing conditions and signal anomalies.

Streaming Data
The transport of streaming data is another function that benefits from AI and machine learning. With so much data moving so quickly there is no way that human observers can watch it all and compensate as necessary—it’s the perfect job for artificial intelligence.

Zixi is one company that uses AI to monitor streaming data and provide alerts and adjustments in the event of signal degradation. “We use AI/ML in two areas: video transport and video content analysis,” said Andrew Broadstone, senior director of product management at Zixi. “Low-level protocol measurements, such as round-trip time, network congestion and retransmission rate, are used to determine link quality and to anticipate upcoming signal degradation.”

Much of what Zixi tests is the transport stream quality, but the company’s quality measurements also drill down into the video encoding to ensure image and sound quality are maintained.

“Certain kinds of content analysis are independent of codec,” Broadstone said. “We also use AI and ML to estimate the perceptual quality of live video [VMAF] without a reference, but for this the video must be H.264/AVC transport stream format. In general, our customers overwhelmingly use H.264 since it is the most compatible format across devices.”

Broadstone said that Zixi’s IDP product uses tens of measurements collected every few seconds across all participants in a video workflow to determine what the company calls its “health score.” This health score lets IDP predict signal path quality and degradation.

“Zixi Health Score is the output of multiple models trained using gradient boosting across the entire Zixi data set with many months of data,” he added. “Zixi Health Score therefore is not a simple set of rules. However, it is typical to see the Health Score drop significantly when there is a sudden change in packet round-trip time, or if raw packet loss steadily increases.”

The aim of Zixi IDP is to anticipate problems and alert operators to the root cause, Broadstone said.

 


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