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:
Aug. 27, 2024

Filed:

Mar. 13, 2019
Applicant:

Netflix, Inc., Los Gatos, CA (US);

Inventors:

Christos Bampis, Los Gatos, CA (US);

Zhi Li, Mountain View, CA (US);

Lavanya Sharan, Menlo Park, CA (US);

Julie Novak, San Jose, CA (US);

Martin Tingley, Los Gatos, CA (US);

Assignee:

NETFLIX, INC., Los Gatos, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06F 18/214 (2023.01); G06N 20/20 (2019.01); G06V 10/774 (2022.01); H04N 19/154 (2014.01); H04N 21/25 (2011.01); H04N 17/00 (2006.01); H04N 19/147 (2014.01);
U.S. Cl.
CPC ...
H04N 21/252 (2013.01); G06F 18/214 (2023.01); G06N 20/20 (2019.01); G06T 7/0002 (2013.01); G06V 10/774 (2022.01); H04N 19/154 (2014.11); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30168 (2013.01); H04N 17/004 (2013.01); H04N 19/147 (2014.11);
Abstract

In various embodiments, a bootstrapping training subsystem performs sampling operation(s) on a training database that includes subjective scores to generate resampled dataset. For each resampled dataset, the bootstrapping training subsystem performs machine learning operation(s) to generate a different bootstrap perceptual quality model. The bootstrapping training subsystem then uses the bootstrap perceptual quality models to quantify the accuracy of a perceptual quality score generated by a baseline perceptual quality model for a portion of encoded video content. Advantageously, relative to prior art solutions in which the accuracy of a perceptual quality score is unknown, the bootstrap perceptual quality models enable developers and software applications to draw more valid conclusions and/or more reliably optimize encoding operations based on the perceptual quality score.


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