Video conferencing requires high-bandwidth, low-latency connections, but can AI help codecs deliver this connectivity more efficiently?

What you’ll learn:

Applying AI to video-conferencing devices.
What is Super-Resolution image expansion?
The impact of deep-learning networks and specter of generative adversarial networks (GANs).
Video conferencing for virtual meetings, distance learning, or socializing has exploded with the onset of the coronavirus pandemic. Some experts suggest that even after the virus recedes, our reliance on virtual gatherings will remain part of our new normality. If so, the huge bandwidth hunger that ubiquitous video conferencing imposes on the internet—from the core out to the thinnest branches—is here to stay.

Even using modern video codecs, a video conference can be demanding on bandwidth: 1 to 2 Mb/s per participant just to keep those thumbnail images on the screen. And there’s growing evidence that with experience, users become more critical of image quality, longing to see fine details of facial expressions, gestures, and posture that carry so much information in an in-person meeting. This trend limits the ability of apps to use higher compression ratios to reduce bandwidth needs. The fine detail the compression algorithm throws out contains just the cues a skilled negotiator needs most.

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