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:
Jul. 12, 2022

Filed:

Nov. 24, 2020
Applicant:

Board of Regents, the University of Texas System, Austin, TX (US);

Inventors:

Alan Bovik, Austin, TX (US);

Sungsoo Kim, Austin, TX (US);

Jin Soo Park, Austin, TX (US);

Christos G. Bampis, Austin, TX (US);

Georgios Alex Dimakis, Austin, TX (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
H04N 19/14 (2014.01); H04N 19/119 (2014.01); H04N 19/172 (2014.01); H04N 19/30 (2014.01); G06N 3/08 (2006.01); H04N 19/42 (2014.01); H04N 19/85 (2014.01); G06N 3/04 (2006.01); H04N 19/124 (2014.01);
U.S. Cl.
CPC ...
H04N 19/14 (2014.11); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); H04N 19/119 (2014.11); H04N 19/124 (2014.11); H04N 19/172 (2014.11); H04N 19/30 (2014.11); H04N 19/42 (2014.11); H04N 19/85 (2014.11);
Abstract

A computer-implemented method, system and computer program product for compressing video. A set of video frames is partitioned into two subsets of different types of frames, a first type and a second type. The first type of frames of videos is compressed to generate a first representation by a first stage encoder. The first representation is then decoded to reconstruct the first type of frames using a first stage decoder. The second type of frames of video is compressed to generate a second representation that only contains soft edge information by a second stage encoder. A generative model corresponding to a second stage decoder is then trained using the first representation and the reconstructed first type of frames by using a discriminator employed by a machine learning system. After training the generative model, it generates reconstructed first and second types of frames using the soft edge information.


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