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. 23, 2022

Filed:

Jun. 09, 2021
Applicant:

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

Inventors:

Oren Rippel, Mountain View, CA (US);

Lubomir Bourdev, Mountain View, CA (US);

Assignee:

WaveOne Inc., Palo Alto, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06N 20/00 (2019.01); H04N 19/126 (2014.01); H04N 19/167 (2014.01); H04N 19/172 (2014.01); H04N 19/196 (2014.01); H04N 19/91 (2014.01); H04N 19/44 (2014.01); H04N 19/13 (2014.01); H04N 19/149 (2014.01); G06N 3/04 (2006.01); G06K 9/62 (2022.01); G06V 10/44 (2022.01); G06V 10/75 (2022.01); G06V 20/40 (2022.01); G06V 20/52 (2022.01); G06V 30/10 (2022.01); G06V 30/194 (2022.01); G06V 40/16 (2022.01); G06T 5/00 (2006.01); H04N 19/18 (2014.01); H04N 19/48 (2014.01); H04N 19/154 (2014.01); H04N 19/33 (2014.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06N 3/04 (2013.01); G06N 20/00 (2019.01); G06K 9/6232 (2013.01); G06K 9/6256 (2013.01); G06K 9/6263 (2013.01); G06K 9/6274 (2013.01); G06N 3/0454 (2013.01); G06N 3/084 (2013.01); G06T 5/002 (2013.01); G06V 10/449 (2022.01); G06V 10/454 (2022.01); G06V 10/758 (2022.01); G06V 20/46 (2022.01); G06V 20/52 (2022.01); G06V 30/10 (2022.01); G06V 30/194 (2022.01); G06V 40/172 (2022.01); 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);
Abstract

A deep learning based compression (DLBC) system applies trained models to compress binary code of an input image to a target codelength. For a set of binary codes representing the quantized coefficents of an input image, the DLBC system applies a first model that is trained to predict feature probabilities based on the context of each bit of the binary codes. The DLBC system compresses the binary code via adaptive arithmetic coding based on the determined probability of each bit. The compressed binary code represents a balance between a reconstruction quality of a reconstruction of the input image and a target compression ratio of the compressed binary code.


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