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. 19, 2021

Filed:

Mar. 05, 2019
Applicants:

Samsung Electronics Co., Ltd., Suwon-si, KR;

Seoul National University R&db Foundation, Seoul, KR;

Inventors:

Hyunseung Lee, Suwon-si, KR;

Donghyun Kim, Suwon-si, KR;

Youngsu Moon, Suwon-si, KR;

Taegyoung Ahn, Suwon-si, KR;

Yoonsik Kim, Seoul, KR;

Jaewoo Park, Seoul, KR;

Jae Woong Soh, Seoul, KR;

Nam Ik Cho, Seoul, KR;

Byeongyong Ahn, Suwon-si, KR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 19/176 (2014.01); G06K 9/40 (2006.01); G06N 3/02 (2006.01); H04N 19/137 (2014.01);
U.S. Cl.
CPC ...
H04N 19/176 (2014.11); G06K 9/40 (2013.01); G06N 3/02 (2013.01); H04N 19/137 (2014.11);
Abstract

An electronic apparatus is provided. The electronic apparatus includes a storage configured to store a compression rate network model configured to determine a compression rate applied to an image block from among a plurality of compression rates, and a plurality of compression noise removing network models configured to remove compression noise for each of the plurality of compression rates, and a processor configured to: obtain a compression rate of each of a plurality of image blocks included in a frame of a decoded moving picture based on the compression rate network model, obtain the compression rate of the frame based on the plurality of obtained compression rates, and remove compression noise of the frame based on a compression noise removing network model corresponding to the compression rate of the frame from among the plurality of compression noise removing network models. The compression rate network model can be obtained by learning image characteristics of a plurality of restored image blocks corresponding to each of the plurality of compression rates through a first artificial intelligence algorithm, and the plurality of restored image blocks can be generated by encoding a plurality of original image blocks, and decoding the encoded plurality of original image blocks, and the plurality of compression noise removing network models can be obtained by learning a relation between the plurality of original image blocks and the plurality of restored image blocks through a second artificial intelligence algorithm.


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