METHOD AND APPARATUS FOR PERFORMING ARTIFICIAL INTELLIGENCE ENCODING AND ARTIFICIAL INTELLIGENCE DECODING

    公开(公告)号:US20210398326A1

    公开(公告)日:2021-12-23

    申请号:US17333845

    申请日:2021-05-28

    Abstract: An apparatus for performing artificial intelligence (AI) encoding on an image includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to: determine a resolution of an original image; when the resolution of the original image is higher than a predetermined value, obtain a first image by performing AI downscaling on the original image via a downscaling deep neural network (DNN); when the resolution of the original image is lower than or equal to the predetermined value, obtain a first image by performing AI one-to-one preprocessing on the original image via a one-to-one preprocessing DNN for upscaling; generate image data by performing first encoding on the first image; and transmit the image data and AI data including information related to the AI downscaling or information related to the AI one-to-one preprocessing.

    METHOD AND APPARATUS FOR STREAMING DATA

    公开(公告)号:US20210358083A1

    公开(公告)日:2021-11-18

    申请号:US17385995

    申请日:2021-07-27

    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.

    METHOD AND APPARATUS FOR STREAMING DATA
    16.
    发明申请

    公开(公告)号:US20200219232A1

    公开(公告)日:2020-07-09

    申请号:US16822665

    申请日:2020-03-18

    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.

    METHODS AND APPARATUSES FOR PERFORMING ARTIFICIAL INTELLIGENCE ENCODING AND ARTIFICIAL INTELLIGENCE DECODING ON IMAGE

    公开(公告)号:US20200184685A1

    公开(公告)日:2020-06-11

    申请号:US16793605

    申请日:2020-02-18

    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.

    METHOD AND APPARATUS FOR ADAPTIVE ARTIFICIAL INTELLIGENCE DOWNSCALING FOR UPSCALING DURING VIDEO TELEPHONE CALL

    公开(公告)号:US20220405884A1

    公开(公告)日:2022-12-22

    申请号:US17893248

    申请日:2022-08-23

    Abstract: Provided is a method of adaptively performing artificial intelligence (AI) downscaling on an image during a video telephone call of a user terminal. The method includes obtaining, from an opposite user terminal, AI upscaling support information of the opposite user terminal that is a target of a video telephone call, determining whether the user terminal is to perform AI downscaling on an original image, based on the AI upscaling support information, based on determining that the user terminal is to perform AI downscaling on the original image, obtaining a first image by AI downscaling the original image using a downscaling deep neural network (DNN), generating image data by performing first encoding on the first image, and transmitting AI data including information related to the AI downscaling and the image data.

    METHOD AND DEVICE FOR EVALUATING SUBJECTIVE QUALITY OF VIDEO

    公开(公告)号:US20210385502A1

    公开(公告)日:2021-12-09

    申请号:US17286743

    申请日:2019-09-26

    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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