US 12,437,201 B2
Predicting optimal values for parameters used in an operation of an image signal processor using machine learning
Younghoon Kim, Suwon-si (KR); Sungsu Kim, Suwon-si (KR); and Jungmin Lee, Suwon-si (KR)
Assigned to SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed by SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed on Dec. 16, 2022, as Appl. No. 18/067,298.
Application 18/067,298 is a continuation of application No. 16/724,626, filed on Dec. 23, 2019, abandoned.
Claims priority of application No. 10-2019-0059573 (KR), filed on May 21, 2019.
Prior Publication US 2023/0117343 A1, Apr. 20, 2023
Int. Cl. G06N 3/084 (2023.01); G06N 3/044 (2023.01); G06N 3/08 (2023.01); G06T 5/50 (2006.01); G06T 5/60 (2024.01); G06T 5/70 (2024.01); G06T 5/92 (2024.01); G06T 7/00 (2017.01)
CPC G06N 3/084 (2013.01) [G06N 3/044 (2023.01); G06N 3/08 (2013.01); G06T 5/50 (2013.01); G06T 5/60 (2024.01); G06T 5/70 (2024.01); G06T 5/92 (2024.01); G06T 7/97 (2017.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 17 Claims
OG exemplary drawing
 
8. An electronic system, comprising:
a simulator including a parameter generator to receive a sample data and generate a plurality of different sample sets and an image signal processor (ISP) simulator to generate sample images based on the plurality of different sample sets, each of the plurality of different sample sets including sample values for a plurality of parameters of the ISP simulator;
an evaluation framework configured to generate a plurality of sample score sets for the sample images;
a machine learning model trainer configured to train a machine learning model by using each of the plurality of different sample sets as an input and adjusting weights included in hidden layers of the machine learning model by comparing an output of the machine learning model and the plurality of sample score sets; and
an ISP parameter adjusting module configured to generate weighted initial parameters by applying initial weights to initial values for parameters of an ISP which is emulated by the ISP simulator, output the weighted initial parameters to the trained machine learning model,
wherein the ISP parameter adjusting module adjusts the initial weights based on a result of comparing outputs of the trained machine learning model and predetermined reference scores, and optimizes the parameters of the ISP based on the adjusted initial weights.