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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.

Patent No.:

US 11729406 B1

PDFFull Text
Date of Patent:
Aug. 15, 2023

Filed:

Mar. 21, 2020

Video compression using deep generative models

Applicant:

Qualcomm Incorporated, San Diego, CA (US);

Inventors:

Amirhossein Habibian, Amsterdam, NL;

Ties Jehan Van Rozendaal, Amsterdam, NL;

Taco Sebastiaan Cohen, Amsterdam, NL;

Assignee:

QUALCOMM INCORPORATED, San Diego, CA (US);

Attorney:

Patterson + Sheridan, LLP

Primary Examiner:

Mohammad J Rahman

Int. Cl.
CPC ...
H04N 19/14 (2014.01); H04N 19/124 (2014.01); H04N 19/179 (2014.01); H04N 19/186 (2014.01); H04N 19/46 (2014.01); H04N 5/247 (2006.01); G06K 9/00 (2022.01); G06K 9/62 (2022.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); H04N 19/20 (2014.01); G06N 3/084 (2023.01); G06V 20/40 (2022.01); G06F 18/21 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); H04N 23/90 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
H04N 19/20 (2014.11); G06F 18/21 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/084 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/46 (2022.01); H04N 19/124 (2014.11); H04N 19/14 (2014.11); H04N 19/179 (2014.11); H04N 19/186 (2014.11); H04N 19/46 (2014.11); H04N 23/90 (2023.01);
Abstract

Certain aspects of the present disclosure are directed to methods and apparatus for compressing video content using deep generative models. One example method generally includes receiving video content for compression. The received video content is generally encoded into a latent code space through an encoder, which may be implemented by a first artificial neural network. A compressed version of the encoded video content is generally generated through a trained probabilistic model, which may be implemented by a second artificial neural network, and output for transmission.

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