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公开(公告)号:US20230245285A1
公开(公告)日:2023-08-03
申请号:US18131643
申请日:2023-04-06
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Quockhanh DINH , Kyonghwan JIN , Youngo PARK , Kwangpyo CHOI
CPC classification number: G06T5/009 , H04N1/6077 , G06V10/44 , G06V10/56 , G06V10/60 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084
Abstract: A device and a method for generating an output image in which a subject has been captured are provided. A method of performing image processing may include: obtaining a raw image by a camera sensor of the device, by using a first processor configured to control the device; inputting the raw image to a first artificial intelligence (AI) model trained to scale image brightness, by using a second processor configured to perform AI-based image processing on the raw image; obtaining tone map data output from the first AI model, by using the second processor; and storing an output image generated based on the tone map data.
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公开(公告)号:US20230044532A1
公开(公告)日:2023-02-09
申请号:US17880799
申请日:2022-08-04
Applicant: SAMSUNG ELECTRONICS CO, LTD.
Inventor: Quockhanh DINH , Kwangpyo CHOI , Minwoo PARK , Yinji PIAO
IPC: H04N19/86 , H04N19/80 , H04N19/182 , H04N19/187 , H04N19/117
Abstract: An image processing method and an image processing apparatus are provided to obtain input data for deblocking filtering based on deblocking filtering target pixels of at least one line perpendicular to a boundary line of blocks and encoding information about the deblocking filtering target pixels of at least one line, obtain a feature map of the input data by inputting the input data to a first neural network, obtain a deblocking filter coefficient by inputting the feature map to a second neural network, obtain a deblocking filter compensation value by inputting the feature map to a third neural network, obtain a deblocking filter strength by inputting the input data to a fourth neural network, obtain deblocking filtered pixels by performing deblocking filtering on the deblocking filtering target pixels of the at least one line using the deblocking filter coefficient, the deblocking filter compensation value, and the deblocking filter strength.
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23.
公开(公告)号:US20230041187A1
公开(公告)日:2023-02-09
申请号:US17882293
申请日:2022-08-05
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Quockhanh DINH , Kwangpyo Choi
IPC: H04N19/436 , H04N19/50 , H04N19/186 , H04N19/136 , H04N19/13 , H04N19/124 , H04N19/30 , G06N3/08 , G06N3/04
Abstract: A method of decoding an image based on cross-channel prediction using artificial intelligence (AI) includes obtaining cross-channel prediction information by applying feature data for cross-channel prediction to a neural-network-based cross-channel decoder, obtaining a predicted image of a chroma image by performing cross-channel prediction based on a reconstructed luma image and the cross-channel prediction information, obtaining a residual image of the chroma image by applying feature data of the chroma image to a neural-network-based chroma residual decoder, and reconstructing the chroma image based on the predicted image and the residual image.
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公开(公告)号:US20220272372A1
公开(公告)日:2022-08-25
申请号:US17677498
申请日:2022-02-22
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Quockhanh DINH , Minwoo Park , Minsoo Park , Kwangpyo Choi
IPC: H04N19/51 , H04N19/137 , H04N19/17 , H04N19/43 , H04N19/91 , H04N19/124
Abstract: A method of reconstructing an optical flow by using artificial intelligence (AI), including obtaining, from a bitstream, feature data of a current residual optical flow for a current image; obtaining the current residual optical flow by applying the feature data of the current residual optical flow to a neural-network-based first decoder; obtaining a current predicted optical flow based on at least one of a previous optical flow, feature data of the previous optical flow, and feature data of a previous residual optical flow; and reconstructing a current optical flow based on the current residual optical flow and the current predicted optical flow.
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公开(公告)号:US20220198628A1
公开(公告)日:2022-06-23
申请号:US17554827
申请日:2021-12-17
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Quockhanh DINH , Kyonghwan JIN , Kwangpyo CHOI
Abstract: An image processing apparatus, including 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: identify, in a previous frame, a prediction sample corresponding to a current sample of a current frame, generate a prediction frame for the current frame by changing a sample value of a collocated sample of the previous frame, wherein the collocated sample of the previous frame is collocated with the current sample, according to a sample value of the prediction sample, derive a weight by comparing a sample value of the current sample with the sample value of the prediction sample, apply the weight to a collocated sample of the prediction frame, wherein the collocated sample of the prediction frame is collocated with the current sample, to obtain a weighted prediction frame, and obtain a current output frame by processing the current frame and the weighted prediction frame through a neural network comprising a convolution layer.
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公开(公告)号:US20210385502A1
公开(公告)日:2021-12-09
申请号:US17286743
申请日:2019-09-26
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Quockhanh DINH , Youngo PARK , Kwangpyo CHOI
IPC: H04N19/85 , H04N19/132 , H04N19/184 , G06N3/02
Abstract: Proposed are a method and apparatus for evaluating the quality of an image, the method including obtaining blocks each having a predetermined size by splitting a target image for evaluating a quality and a reference image that is to be compared with the target image, determining sensitivity information and quality assessment information of each of the blocks by inputting the blocks to a video quality assessment network, and determining a final image quality assessment score of the target image by combining the pieces of quality assessment information of the blocks with each other, based on the pieces of sensitivity information of the blocks.
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公开(公告)号:US20210042882A1
公开(公告)日:2021-02-11
申请号:US17079773
申请日:2020-10-26
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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28.
公开(公告)号:US20200219292A1
公开(公告)日:2020-07-09
申请号:US16821609
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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