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公开(公告)号：US20240185603A1
公开(公告)日：2024-06-06
申请号：US18379969
申请日：2023-10-13
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Namuk KIM , Cheuihee HAHM , Jayoon KOO , Wookhyung KIM , IIhyun CHO
CPC classification number: G06V20/41 , G06V10/225 , G06V10/82 , G06V20/46
Abstract: Provided are an electronic device and a control method thereof. The electronic device includes at least one memory storing at least one instruction; and at least one processor connected to the at least one memory and configured to execute the at least one instruction to: input information about a first frame among a plurality of frames to a first object detection network and obtain first information about an object included in the first frame, store the first information in the at least one memory, and input the first information and information about a second frame among the plurality of frames to a second object detection network and obtain second information about an object included in the second frame, wherein the second frame is a next frame following the first frame.
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2.发明公开公开(公告)号：US20230177638A1
公开(公告)日：2023-06-08
申请号：US17312276
申请日：2021-01-11
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Wookhyung KIM , Ilhyun CHO , Jayoon KOO , Namuk KIM
CPC classification number: G06T3/4046 , G06T3/4053 , G06V10/761 , G06V10/82
Abstract: An artificial intelligence (AI) upscaling apparatus for upscaling a low-resolution image to a high-resolution image includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is configured to: obtain a second image corresponding to a first image, which is downscaled from an original image by an AI downscaling apparatus by using a first deep neural network (DNN); and obtain a third image by upscaling the second image by using a second DNN corresponding to the first DNN, and wherein the second DNN is trained to minimize a difference between a first restored image, which results from applying no pixel movement to an original training image, and second restored images, which result from downscaling, upscaling, and subsequently retranslating one or more translation images obtained by applying pixel movement to the original training image.
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3.发明公开公开(公告)号：US20240265493A1
公开(公告)日：2024-08-08
申请号：US18638310
申请日：2024-04-17
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Wookhyung KIM , Ilhyun CHO , Jayoon KOO , Namuk KIM
IPC: G06T3/4046 , G06T3/4053
CPC classification number: G06T3/4046 , G06T3/4053
Abstract: An artificial intelligence (AI) upscaling apparatus for upscaling a low-resolution image to a high-resolution image includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is configured to: obtain a second image corresponding to a first image, which is downscaled from an original image by an AI downscaling apparatus by using a first deep neural network (DNN); and obtain a third image by upscaling the second image by using a second DNN corresponding to the first DNN, and wherein the second DNN is trained to minimize a difference between a first restored image, which results from applying no pixel movement to an original training image, and second restored images, which result from downscaling, upscaling, and subsequently retranslating one or more translation images obtained by applying pixel movement to the original training image.
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4.发明公开公开(公告)号：US20240135697A1
公开(公告)日：2024-04-25
申请号：US18367193
申请日：2023-09-11
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Ilhyun CHO , Wookhyung KIM , Jayoon KOO , Namuk KIM
Abstract: An electronic apparatus includes a memory configured to store a neural network model including a first network and a second network. The electronic apparatus also includes at least one processor connected to the memory. The at least one processor is configured to obtain description information corresponding to a first image by inputting the first image to the first network, obtain a second image based on the description information, obtain a third image representing a region of interest of the first image by inputting the first image and the second image to the second network. The neural network model is a model trained based on a plurality of sample images, a plurality of sample description information corresponding to the plurality of sample images, and a sample region of interest of the plurality of sample images.
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5.发明公开公开(公告)号：US20240233356A9
公开(公告)日：2024-07-11
申请号：US18367193
申请日：2023-09-12
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Ilhyun CHO , Wookhyung KIM , Jayoon KOO , Namuk KIM
Abstract: An electronic apparatus includes a memory configured to store a neural network model including a first network and a second network. The electronic apparatus also includes at least one processor connected to the memory. The at least one processor is configured to obtain description information corresponding to a first image by inputting the first image to the first network, obtain a second image based on the description information, obtain a third image representing a region of interest of the first image by inputting the first image and the second image to the second network. The neural network model is a model trained based on a plurality of sample images, a plurality of sample description information corresponding to the plurality of sample images, and a sample region of interest of the plurality of sample images.
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Patent Agency Ranking
公开(公告)号：US20220392210A1
公开(公告)日：2022-12-08
申请号：US17824587
申请日：2022-05-25
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Wookhyung KIM , Anant BAIJAL , Cheulhee HAHM , Namuk KIM , Jayoon KOO , Ilhyun CHO
Abstract: An electronic device is provided. The electronic device includes a memory storing one or more instructions, and a processor configured to execute the one or more instruction stored in the memory. The processor is configured to execute the one or more instructions to obtain a subjective assessment score for each of a plurality of sub-regions included in an input frame, the subjective assessment score being a Mean Opinion Score (MOS); obtain a location weight for each of the plurality of sub-regions, the location weight indicating characteristics according to a location of a display; obtain a weighted assessment score for each of the plurality of sub-regions, based on the subjective assessment score for each of the plurality of sub-regions and the location weight for each of the plurality of sub-regions; and obtain a final quality score for the entire video frame, based on the weighted assessment score for each of the plurality of sub-regions.
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公开(公告)号：US20220300236A1
公开(公告)日：2022-09-22
申请号：US17835702
申请日：2022-06-08
Applicant: Samsung Electronics Co., Ltd.
Inventor： Anant BAIJAL , Cheulhee HAHM , Jayoon KOO , Seungwon CHA , Namuk KIM , Wookhyung KIM , Minki LEE
Abstract: Provided is a display device including a display configured to display first content; a memory storing instructions; and a processor configured to execute the instructions to: obtain first content information by analyzing the first content, obtain user tracking information by tracking a user viewing the first content displayed by the display, determine whether a trigger condition is satisfied, based on at least one of the first content information or the user tracking information, and based on the trigger condition being satisfied, control a communication unit to transmit second content to a projector connected to the display device, wherein the second content is generated based on the first content.
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公开(公告)号：US20240420463A1
公开(公告)日：2024-12-19
申请号：US18814120
申请日：2024-08-23
Applicant: SAMSUNG ELECTRONICS CO.,LTD.
Inventor： Wookhyung KIM , Cheulhee HAHM , Namuk KIM , Anant BAIJAL , Jayoon KOO , IIhyun CHO
Abstract: An electronic device includes a memory configured to store a trained neural network model and a processor configured to, by inputting an input image to the trained neural network model, obtain a quality score of the input image, a pixel quality score for each pixel included in the input image and a Region of Interest (ROI) score for the each pixel. The trained neural network obtains feature information for each pixel in the input image, a quality score of each pixel, and an ROI score for the each pixel. A computation module obtains an image quality score for the input image based on pixel quality score and the ROI score for the each pixel.
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公开(公告)号：US20240378714A1
公开(公告)日：2024-11-14
申请号：US18781512
申请日：2024-07-23
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Wookhyung KIM , Jayoon KOO , Namuk KIM , Ilhyun CHO
IPC: G06T7/00 , G06T5/20 , G06V10/771
Abstract: Provided are an electronic apparatus for evaluating the quality of an image and an operating method of the electronic apparatus. The electronic apparatus includes a memory in which at least one instruction is stored and at least one processor configured to execute the at least one instruction stored in the memory to obtain the image, extract a feature map including a feature of the image based on the image, calculate a quality score for each reference region of the image based on the extracted feature map, calculate an importance for each reference region of the image based on the extracted feature map, and evaluate the quality of the image according to a final quality score of the image calculated based on the quality score for each reference region and the importance for each reference region.
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公开(公告)号：US20220101123A1
公开(公告)日：2022-03-31
申请号：US17364157
申请日：2021-06-30
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Namuk KIM , Anant BAIJAL , Jayoon KOO , Ilhyun CHO , Cheulhee HAHM , Wookhyung KIM , Keuntek LEE
Abstract: An operating method of a computing apparatus is provided. The operating method of the computing apparatus includes obtaining a reference image; obtaining a distorted image generated from a reference image; obtaining an objective quality assessment score of a distorted image that is indicative of a quality of a distorted image as assessed by an algorithm, by using a reference image and a distorted image; obtaining a subjective quality assessment score corresponding to a objective quality assessment score; and training a neural network, by using a distorted image and a subjective quality assessment score as a training data set.
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