Google Duo Adds Built-In Screenshots, AV1 Codec Support And Huge Surge In Growth

Due to COVID-19, people are doing video conferencing a lot which led to the recent growth of Google Duo. To start, Google Duo getting 10 million new sign-ups per week with a ten-fold increase in called minutes for many countries. Meanwhile, the built-in messaging feature that lets you create a video with AR effects, voice clips, and simple notes with text and doodles is similarly growing.

People are sending 180% more messages in general, while there’s an 800% increase in “regions particularly impacted by social distancing.” The duo is soon adding the ability to automatically save messages that will coexist with the current 24-hour ephemeral option. The company says this is designed to preserve particularly meaningful messages.

In terms of core functionality, Duo is adding a snapshot feature when calling that replaces manual screenshots. A new shutter button in the bottom-left corner will take a photo of both streams and automatically share the full side-by-side image with participants.
Available on smartphones, tablets, and Chromebooks, this new Google Duo feature is rolling out today for one-on-one conversations and later coming to more devices, as well as group calls.

Google touts an 8x increase in the number of Duo group conversations over the past four weeks. After raising the participant limit from 8 people to 12 last month, Google plans to boost that size “even further” over the “coming weeks.” For comparison, FaceTime supports up to 32 callers.

On the backend, Google announced that Duo will soon switch to the AV1 video codec to “improve video call quality and reliability.” In the example below, we see an incoming video call (on the left) at 30kbps using the new AV1 tech.
This Google-backed standard — in use by YouTube and Netflix — provides 30% better performance for low bandwidth Duo users on Wi-Fi and cellular networks. AV1 starts rolling out this week on Android and to the iOS app in two weeks. This follows Google leveraging machine learning to reduce audio interruptions by filling in missing sounds.
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