METHOD FOR GENERATING SUPER-RESOLUTION VIDEO USING MULTI-CAMERA VIDEO TRIPLETS AND DEVICE FOR THE SAME

    公开(公告)号:US20240212093A1

    公开(公告)日:2024-06-27

    申请号:US18396411

    申请日:2023-12-26

    CPC classification number: G06T3/4053 G06T3/4046

    Abstract: A method for generating a super-resolution video by using a multi-camera video may comprise: generating a resolution-improved ultra-wide-angle video frame at an arbitrary time step by inputting an ultra-wide-angle video frame of a first resolution at the arbitrary time step, ultra-wide-angle video frames right before and right after the arbitrary time step, and a wide-angle video frame for reference at the arbitrary time step, to a bidirectional neural network, wherein the generating of the resolution-improved ultra-wide-angle video frame is performed using accumulated information at a past time step based on the arbitrary time step, and accumulated information at a future time step based on the arbitrary time step, and wherein a second resolution, which is a resolution of the generated ultra-wide-angle video frame, is greater than the first resolution.

    METHOD AND APPARATUS FOR ENHANCING VIDEO QUALITY BASED ON MACHINE LEARNING

    公开(公告)号:US20220198616A1

    公开(公告)日:2022-06-23

    申请号:US17497824

    申请日:2021-10-08

    Abstract: A video quality improvement method may comprise: inputting a structure feature map converted from current target frame by first convolution layer to first multi-task unit and second multi-task unit, which is connected to an output side of first multi-task unit, among the plurality of multi-task units; inputting a main input obtained by adding the structure feature map to a feature space, which is converted by second convolution layer from those obtained by concatenating, in channel dimension, a previous target frame and a correction frame of the previous frame to first multi-task unit; and inputting current target frame to Nth multi-task unit connected to an end of output side of second multi-task unit, wherein Nth multi-task unit outputs a correction frame of current target frame, and machine learning of the video quality improvement model is performed using an objective function calculated through the correction frame of current target frame.

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