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:
Jan. 16, 2024

Filed:

Jul. 01, 2021
Applicant:

Tencent America Llc, Palo Alto, CA (US);

Inventors:

Wei Jiang, Sunnyvale, CA (US);

Wei Wang, San Jose, CA (US);

Ding Ding, Palo Alto, CA (US);

Shan Liu, San Jose, CA (US);

Xiaozhong Xu, State College, PA (US);

Assignee:

TENCENT AMERICA LLC, Palo Alto, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 19/42 (2014.01); H04N 19/147 (2014.01); G06F 18/214 (2023.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
H04N 19/42 (2014.11); G06F 18/214 (2023.01); G06V 10/82 (2022.01); H04N 19/147 (2014.11);
Abstract

A method of task-adaptive pre-processing (TAPP) for neural image compression is performed by at least one processor and includes generating a substitutional image, based on an input image, using a TAPP neural network, and encoding the generated substitutional image to generate a compressed representation, using a first neural network. The TAPP neural network is trained by generating a substitutional training image, based on an input training image, using the TAPP neural network, encoding the generated substitutional training image to generate a compressed training representation, using the first neural network, decoding the generated compressed training representation to reconstruct an output training image, using a second neural network, generating gradients of a rate-distortion (R-D) loss that is generated based on the input training image, the reconstructed output training image and the generated compressed training representation, and updating the generated substitutional training image, based on the generated gradients of the R-D loss.


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