The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 13, 2022

Filed:

Sep. 30, 2020
Applicant:

Amazon Technologies, Inc., Seattle, WA (US);

Inventors:

Hai Wei, Seattle, WA (US);

Charles Benjamin Waggoner, Portland, OR (US);

Zongyi Liu, Redmond, WA (US);

Srinivas Rajagopalan, Seattle, WA (US);

Lei Li, Yarrow Point, WA (US);

Assignee:

Amazon Technologies, Inc., Seattle, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 17/00 (2006.01); G06K 9/62 (2022.01); G06T 7/00 (2017.01); G06V 20/40 (2022.01);
U.S. Cl.
CPC ...
H04N 17/00 (2013.01); G06K 9/6256 (2013.01); G06T 7/0002 (2013.01); G06V 20/46 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30168 (2013.01);
Abstract

Techniques for content-adaptive video sampling for automated video quality monitoring are described. As one example, a computer-implemented method includes receiving a request to train a machine learning model on a training video file comprising at least one labeled defect, performing an encode on the training video file to generate one or more compression features for each compressed frame of the training video file, training the machine learning model to identify a proper subset of candidate defect frames of the training video file based at least in part on the one or more compression features for each compressed frame of the training video file and the at least one labeled defect, receiving an inference request for an input video file, performing an encode on the input video file to generate one or more compression features for each compressed frame of the input video file, generating, by the machine learning model, a proper subset of candidate defect frames of the input video file based at least in part on the one or more compression features for each compressed frame of the input video file, and determining a defect in the input video file based at least in part on the proper subset of candidate defect frames of the input video file.


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