Method and apparatus for streaming data

    公开(公告)号:US11704770B2

    公开(公告)日:2023-07-18

    申请号:US17385995

    申请日:2021-07-27

    CPC classification number: G06T3/4046 H04L65/70 H04L65/762 H04L65/80 H04N19/85

    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 device for evaluating subjective quality of video

    公开(公告)号:US11616988B2

    公开(公告)日:2023-03-28

    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.

    DEVICE AND METHOD FOR ENCODING MOTION VECTOR, AND DEVICE AND METHOD FOR DECODING MOTION VECTOR

    公开(公告)号:US20220174288A1

    公开(公告)日:2022-06-02

    申请号:US17675410

    申请日:2022-02-18

    Abstract: A method, performed by an image decoding apparatus, of decoding a motion vector, including obtaining information indicating a motion vector resolution of a current block from a bitstream; selecting a first neighboring block from among neighboring blocks adjacent to the current block, by using the obtained information indicating the motion vector resolution of the current block; based on the current block referring to a reference picture in a list 0, and the first neighboring block referring to the reference picture in the list 0, determining a prediction motion vector of the current block using a motion vector of the first neighboring block; based on the current block referring to the reference picture in the list 0 and the first neighboring block referring to a reference picture in a list 1, selecting a motion vector of a second neighboring block among the neighboring blocks as a basic motion vector, and determining the prediction motion vector of the current block using the determined basic motion vector; and determining a motion vector of the current block using the prediction motion vector of the current block.

    Method and apparatus for streaming data

    公开(公告)号:US11170473B2

    公开(公告)日:2021-11-09

    申请号:US17080543

    申请日:2020-10-26

    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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