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:
Oct. 17, 2023

Filed:

Mar. 23, 2022
Applicant:

Novatek Microelectronics Corp., Hsinchu, TW;

Inventors:

Jen-Huan Hu, Taipei, TW;

Wei-Ting Chen, Tainan, TW;

Yu-Che Hsiao, Kaohsiung, TW;

Shih-Hsiang Lin, Hsinchu County, TW;

Po-Chin Hu, Hsinchu County, TW;

Yu-Tsung Hu, Changhua County, TW;

Pei-Yin Chen, Tainan, TW;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/80 (2017.01); G06T 7/70 (2017.01); G06T 5/00 (2006.01); G06T 3/40 (2006.01); G06N 3/08 (2023.01); G06V 20/40 (2022.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06N 3/045 (2023.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06T 5/009 (2013.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 3/4046 (2013.01); G06V 10/82 (2022.01); G06V 20/41 (2022.01); G06V 20/46 (2022.01);
Abstract

A training method for video stabilization and an image processing device using the same are proposed. The method includes the following steps. An input video including low dynamic range (LDR) images is received. The LDR images are converted to high dynamic range (HDR) images by using a first neural network. A feature extraction process is performed to obtain features based on the LDR images and the HDR images. A second neural network for video stabilization is trained according to the LDR images and the HDR images based on a loss function by minimizing a loss value of the loss function to generate stabilized HDR images in a time-dependent manner, where the loss value of the loss function depends upon the features. An HDR classifier is constructed according to the LDR images and the HDR images. The stabilized HDR images are classified by using the HDR classifier to generate a reward value, where the loss value of the loss function further depends upon the reward value.


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