公开(公告)号：US11949858B2

公开(公告)日：2024-04-02

申请号：US16895232

申请日：2020-06-08

Applicant: QUALCOMM Incorporated

Inventor： Aniket Anil Masule ,  Rajeshwar Kurapaty ,  Vikash Garodia

IPC: H04N19/114 ,  H04N19/139 ,  H04N19/177

CPC classification number: H04N19/114 ,  H04N19/139 ,  H04N19/177

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.

公开(公告)号：US20230077283A1

公开(公告)日：2023-03-09

申请号：US17468177

申请日：2021-09-07

Applicant: QUALCOMM Incorporated

Inventor： Uma Mehta ,  Vishnu Priyanka Gujjula ,  Rajeshwar Kurapaty ,  Vikash Garodia ,  Malathi Gottam

IPC: H04M3/56 ,  G10L17/18

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.

公开(公告)号：US20210385443A1

公开(公告)日：2021-12-09

申请号：US16895232

申请日：2020-06-08

Applicant: QUALCOMM Incorporated

Inventor： Aniket Anil Masule ,  Rajeshwar Kurapaty ,  Vikash Garodia

IPC: H04N19/114 ,  H04N19/177 ,  H04N19/139

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