Snap Inc.

Amazon Technologies, Inc.

Richard Leider

Sergey Tulyakov

Jiayao Yu

Jian Ren

Benjamin Dodson

Francesc Xavier Drudis Rius

Angus Kong

Kavya Venkata Kota Kopparapu

Hassan Heidari Namarvar

Ryan Bickhart

Tarek Amara

Krishnakanth Rapaka

Sunil Gopal Koteyar

John Bowers

Techniques and systems for adaptive bitrate optimization for live content delivery are disclosed. In some embodiments, such techniques may include extracting, in real-time, features from content segments of a video stream; for each content segment of the video stream, identifying a rate-distortion (RD) cluster from a mapping of extracted features to RD clusters using one or more trained machine learning models; and transcoding the video stream by applying, to the content segments, a transcoding ladder having bitrate information corresponding to each identified RD cluster.

Adaptive bitrate optimization for content delivery

Aspects of the present disclosure involve a system comprising a medium storing a program and method for machine-learning based selection of a representative video frame. The program and method provide for receiving a set of video frames; determining a first subset of frames by removing frames outside of an image quality threshold; determining a second subset by removing frames outside of an image stillness threshold; computing feature data for each frame in the second subset; providing, for each frame in the second subset, the feature data to a machine learning model (MLM), the MLM being configured to output a score for each frame in the second subset of frames based on the feature data, the MLM having been trained with a first set of images labeled based on aesthetics, and with a second set of images labeled based on image quality; and selecting a frame based on output scores.

Machine learning-based selection of a representative video frame within a messaging application

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San Francisco, CA, United States of America

Richard Leider