Abstract: Methods, systems, and devices for improved video throughput using deep learning video coding are described. A device may receive a bitstream including a set of video frames. The device may batch the set of video frames into a first subset of video frames and a second subset of video frames based on a change in a reference scene associated with the set of video frames. The device may select a mode of operation for a neural processing unit of the device based on the batching. The device may generate a set of video packets including the first subset of video frames, the second subset of video frames, or both, based on the neural processing unit and the selected mode of operation.

Abstract: In some aspects, a device may obtain first sensor data from a sensor configured to detect a characteristic associated with the device. The device may cause automatic implementation of a first setting for a feature of the device that is controllable by a user, the first setting based at least in part on the first sensor data. The device may detect a user-controlled change to the first setting for the feature. The device may obtain second sensor data from the sensor. The device may cause automatic implementation of a second setting, for the feature, that is identified by a machine learning model based at least in part on the second sensor data. The machine learning model may be trained to identify a setting for the feature based at least in part on information relating to the user-controlled change to the first setting. Numerous other aspects are described.

Abstract: Techniques for controlling an audio conference include receiving audio data from a participant in the audio conference, analyzing the audio data to determine one or more of a speaker of the audio data or a context of the audio data to produce an analysis of the audio data, and controlling a microphone or adjusting the audio data of the participant based on the analysis of the audio data. The microphone may be muted based on a determination that the speaker is not the participant or the content of the audio is outside of the context of the audio conference.

Abstract: Methods, systems, and devices for improved video throughput using deep learning video coding are described. A device may receive a bitstream including a set of video frames. The device may batch the set of video frames into a first subset of video frames and a second subset of video frames based on a change in a reference scene associated with the set of video frames. The device may select a mode of operation for a neural processing unit of the device based on the batching.

Abstract: Methods, systems, and devices for efficient bandwidth usage during video communications are described. A device may estimate first motion vector information of a frame associated with a set of video frames based on a reference frame associated with the set of video frames. The reference frame may include a preceding intra-frame, a predicted-frame, or a bi-directional predicted frame in a video frame sequence. In some aspects, the device may estimate second motion vector information of the frame associated with the set of video frames based on a learning model, compare the first motion vector information and the second motion vector information using the learning model, and generate a set of video packets carrying the set of video frames including the video frame based on the comparing. In some examples, the video frame may be generated at the device or at a second device in wireless communication with the device.