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11.发明申请公开(公告)号：US20200226717A1
公开(公告)日：2020-07-16
申请号：US16831521
申请日：2020-03-26
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Jaehwan KIM , Jongseok LEE , Sunyoung JEON , Kwangpyo CHOI , Minseok CHOI , Quockhanh DINH , Youngo PARK
Abstract: An artificial intelligence (AI) decoding apparatus includes a memory storing one or more instructions, and a processor configured to execute the stored one or more instructions, to obtain image data corresponding to a first image that is encoded, obtain a second image corresponding to the first image by decoding the obtained image data, determine whether to perform AI up-scaling of the obtained second image, based on the AI up-scaling of the obtained second image being determined to be performed, obtain a third image by performing the AI up-scaling of the obtained second image through an up-scaling deep neural network (DNN), and output the obtained third image, and based on the AI up-scaling of the obtained second image being determined to be not performed, output the obtained second image.
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12.发明申请公开(公告)号：US20200219293A1
公开(公告)日：2020-07-09
申请号：US16821686
申请日：2020-03-17
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Jaehwan KIM , Jongseok LEE , Sunyoung JEON , Kwangpyo CHOI , Minseok CHOI , Quockhanh DINH , Youngo PARK
Abstract: Provided is an artificial intelligence (AI) decoding apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, the processor is configured to: obtain AI data related to AI down-scaling an original image to a first image; obtain image data corresponding to an encoding result on the first image; obtain a second image corresponding to the first image by performing a decoding on the image data; obtain deep neural network (DNN) setting information among a plurality of DNN setting information from the AI data; and obtain, by an up-scaling DNN, a third image by performing the AI up-scaling on the second image, the up-scaling DNN being configured with the obtained DNN setting information, wherein the plurality of DNN setting information comprises a parameter used in the up-scaling DNN, the parameter being obtained through joint training of the up-scaling DNN and a down-scaling DNN, and wherein the down-scaling DNN is used to obtain the first image from the original image.
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13.发明申请公开(公告)号：US20200151914A1
公开(公告)日：2020-05-14
申请号：US16743618
申请日：2020-01-15
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Quockhanh DINH , Minseok CHOI , Kwangpyo CHOI
Abstract: Provided is an artificial intelligence (AI) decoding apparatus including a memory storing one or more instructions; and a processor configured to execute the one or more instructions to, when an image is input to a second DNN including a plurality of layers, obtain first result values based on an operation between the image and a first filter kernel and obtain second result values based on an operation between the image and a second filter kernel, from a first layer including the first and second filter kernels from among the plurality of layers, perform normalization by transforming the first result values into first values by using a first scale factor, and, perform normalization by transforming the second result values into second values by using a second scale factor, transform the first values and the second values into integer values included in a preset range.
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公开(公告)号：US20240233092A1
公开(公告)日：2024-07-11
申请号：US18616953
申请日：2024-03-26
Applicant: Samsung Electronics Co., Ltd.
Inventor： Gyehyun KIM , Beomseok KIM , Youjin LEE , Taeyoung JANG , Youngo PARK , Yongsup PARK , Sangmi LEE , Kwangpyo CHOI
CPC classification number: G06T5/60 , G06T5/50 , G06T2207/20016 , G06T2207/20084 , G06T2207/20216
Abstract: Provided are an image processing method and an input processing device based on a neural network, the method including: obtaining a feature map distinguishing between a near object and a distant object of a low-resolution input image, obtaining a composited weight map for the low-resolution input image by inputting the feature map to a first Deep Neural Network (DNN), obtaining a first image by inputting the low-resolution input image to a second DNN suitable for restoring a distant object, obtaining a second image by inputting the low-resolution input image to a third DNN suitable for restoring a near object, and obtaining a high-resolution image for the low-resolution input image by performing weighted averaging on the first image and the second image using the composited weight map.
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公开(公告)号：US20240048711A1
公开(公告)日：2024-02-08
申请号：US18232209
申请日：2023-08-09
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Yinji PIAO , Kyungah KIM , Minsoo PARK , Minwoo PARK , Kwangpyo CHOI
IPC: H04N19/132 , H04N19/186 , H04N19/176 , H04N19/46
CPC classification number: H04N19/132 , H04N19/186 , H04N19/176 , H04N19/46
Abstract: An artificial intelligence (AI)-based video decoding method comprises obtaining, from a bitstream, a joint chroma residual sample of a current block, Cb component prediction information of the current block, and Cr component prediction information of the current block; determining a prediction sample of the Cb component of the current block based on at least the Cb component prediction information; determining a prediction sample of the Cr component of the current block based on at least the Cr component prediction information; and reconstructing the current block by obtaining a reconstructed sample of the Cb component of the current block and a reconstructed sample of the Cr component of the current block from an output of a neural network by inputting the joint chroma residual sample, the prediction sample of the Cb component, and the prediction sample of the Cr component to the neural network.
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Patent Agency Ranking
16.发明公开公开(公告)号：US20230281458A1
公开(公告)日：2023-09-07
申请号：US18174976
申请日：2023-02-27
Applicant: Samsung Electronics Co., Ltd.
Inventor： Aviral AGRAWAL , Raj Narayana GADDE , Anubhav SINGH , Yinji PIAO , Minwoo PARK , Kwangpyo CHOI
Abstract: A method and an electronic device for low-complexity in-loop filter inference using feature-augmented training are provided. The method includes combining spatial and spectral domain features, using spectral domain features for global feature extraction and signalling to the spatial stream during training, using a detachable spectral domain stream for differential complexity during training versus inference, and combining a unique set of losses resulting from multi-stream and multi-feature approaches to obtain an optimal output.
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公开(公告)号：US20230031143A1
公开(公告)日：2023-02-02
申请号：US17723055
申请日：2022-04-18
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Sangwook BAEK , Sangwon LEE , Taekeun KANG , Dongkyu KIM , Gihyeon BAE , Jungmin LEE , Youngo PARK , Kwangpyo CHOI
Abstract: An image processing apparatus for performing image quality processing on an image includes: a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to: obtain a first image by downscaling an input image by using a downscale network; extract first feature information corresponding to the first image by using a feature extraction network; obtain a second image by performing image quality processing on the first image based on the first feature information, by using an image quality processing network; and obtain an output image by upscaling the second image, extracting second feature information corresponding to the input image, and performing image quality processing on the upscaled second image based on the second feature information, by using an upscale network.
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公开(公告)号：US20210390659A1
公开(公告)日：2021-12-16
申请号：US17237775
申请日：2021-04-22
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Jaehwan KIM , Youngo Park , Kwangpyo CHOI
Abstract: An artificial intelligence (AI) encoding apparatus includes a processor configured to execute one or more instructions stored in the AI encoding apparatus to: input, to a downscale deep neural network (DNN), a first reduced image downscaled from an original image and a reduction feature map having a resolution lower than a resolution of the original image; obtain a first image AI-downscaled from the original image in the downscale DNN; generate image data by performing a first encoding process on the first image; and output the image data.
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公开(公告)号：US20210118189A1
公开(公告)日：2021-04-22
申请号：US17082848
申请日：2020-10-28
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Jaehwan KIM , Jongseok LEE , Sunyoung JEON , Kwangpyo CHOI , Minseok CHOI , Quockhanh DINH , Youngo PARK
Abstract: Provided is an artificial intelligence (AI) decoding apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, the processor is configured to: obtain AI data related to AI down-scaling an original image to a first image; obtain image data corresponding to an encoding result on the first image; obtain a second image corresponding to the first image by performing a decoding on the image data; obtain deep neural network (DNN) setting information among a plurality of DNN setting information from the AI data; and obtain, by an up-scaling DNN, a third image by performing the AI up-scaling on the second image, the up-scaling DNN being configured with the obtained DNN setting information, wherein the plurality of DNN setting information comprises a parameter used in the up-scaling DNN, the parameter being obtained through joint training of the up-scaling DNN and a down-scaling DNN, and wherein the down-scaling DNN is used to obtain the first image from the original image.
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公开(公告)号：US20200219233A1
公开(公告)日：2020-07-09
申请号：US16824486
申请日：2020-03-19
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Youngo PARK , Yumi SOHN , Myungjin EOM , Kwangpyo CHOI
Abstract: A terminal for receiving streaming data may receive information of a plurality of different quality versions of an image content; request, based on the information, a server for a version of the image content from among the plurality of different quality versions of the image content; when the requested version of the image content and artificial intelligence (AI) data corresponding to the requested version of the image content are received, determines whether to perform AI upscaling on the received version of the image content, based on the AI data; and based on a result of the determining whether to perform AI upscaling, performs AI upscaling on the received version of the image content through a upscaling deep neural network (DNN) that is trained jointly with a downscaling DNN of the server.
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