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:
Nov. 14, 2023

Filed:

Jul. 20, 2021
Applicant:

Deepnorth Inc., Foster City, CA (US);

Inventors:

Jinjun Wang, San Jose, CA (US);

Yudong Liang, Xi'an, CN;

Assignee:

DEEP NORTH, INC., Redwood City, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06N 3/084 (2023.01); G06T 7/33 (2017.01); G06V 10/44 (2022.01); G06V 10/98 (2022.01); G06F 18/213 (2023.01); G06F 18/2413 (2023.01); G06N 3/045 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G06F 18/213 (2023.01); G06F 18/24133 (2023.01); G06N 3/045 (2023.01); G06T 7/0002 (2013.01); G06T 7/337 (2017.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/993 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30168 (2013.01);
Abstract

A system for image quality assessment of non-aligned images includes a first deep path portion of a convolutional neural network having a set of parameters and a second deep path portion of the convolutional neural network sharing a set of parameters with the first deep path convolutional neural network. Weights are shared between the first and second deep path convolutional neural networks to support extraction of a same set of features in each neural network pathway. Non-aligned reference and distorted images are respectively provided to the first and second deep paths of the convolutional neural network for processing. A concatenation layer is connected to both the first and second deep paths convolutional neural network, and a fully connected layer is connected to the concatenation layer to receive input from both the first and second deep paths of the convolutional neural network, generating an image quality assessment as a linear regressor and outputting an image quality score.


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