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. 03, 2019

Filed:

Dec. 15, 2017
Applicant:

Waveone Inc., Mountain View, CA (US);

Inventors:

Oren Rippel, Mountain View, CA (US);

Lubomir Bourdev, Mountain View, CA (US);

Carissa Lew, San Jose, CA (US);

Sanjay Nair, Fremont, CA (US);

Assignee:

WaveOne Inc., Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 19/167 (2014.01); H04N 19/126 (2014.01); H04N 19/172 (2014.01); H04N 19/196 (2014.01); H04N 19/91 (2014.01); G06N 3/08 (2006.01); G06K 9/62 (2006.01); G06K 9/00 (2006.01); G06N 3/04 (2006.01); G06N 20/00 (2019.01); H04N 19/44 (2014.01); G06K 9/66 (2006.01); G06T 5/00 (2006.01); H04N 19/13 (2014.01); H04N 19/149 (2014.01); H04N 19/18 (2014.01); H04N 19/48 (2014.01); H04N 19/154 (2014.01); H04N 19/33 (2014.01); G06K 9/46 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06K 9/00288 (2013.01); G06K 9/00744 (2013.01); G06K 9/00771 (2013.01); G06K 9/4619 (2013.01); G06K 9/4628 (2013.01); G06K 9/6212 (2013.01); G06K 9/6232 (2013.01); G06K 9/6256 (2013.01); G06K 9/6263 (2013.01); G06K 9/6274 (2013.01); G06K 9/66 (2013.01); G06N 3/04 (2013.01); G06N 3/084 (2013.01); G06N 20/00 (2019.01); G06T 5/002 (2013.01); H04N 19/126 (2014.11); H04N 19/13 (2014.11); H04N 19/149 (2014.11); H04N 19/154 (2014.11); H04N 19/167 (2014.11); H04N 19/172 (2014.11); H04N 19/18 (2014.11); H04N 19/197 (2014.11); H04N 19/33 (2014.11); H04N 19/44 (2014.11); H04N 19/48 (2014.11); H04N 19/91 (2014.11); G06K 2209/01 (2013.01);
Abstract

A compression system trains a machine-learned encoder and decoder. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder receives content and generates a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder receives a tensor and generates a reconstructed version of the content. In one embodiment, the compression system trains one or more encoding components such that the encoder can adaptively encode different degrees of information for regions in the content that are associated with characteristic objects, such as human faces, texts, or buildings.


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