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    DIFFUSION-BASED DATA COMPRESSION
    1.
    发明公开
    DIFFUSION-BASED DATA COMPRESSION 审中-公开

    公开(公告)号：US20240121398A1

    公开(公告)日：2024-04-11

    申请号：US18458006

    申请日：2023-08-29

    申请人： QUALCOMM Incorporated

    发明人： Noor Fathima Khanum MOHAMED GHOUSE ,  Jens PETERSEN ,  Tianlin XU ,  Guillaume Konrad SAUTIERE ,  Auke Joris WIGGERS

    IPC分类号： H04N19/137 ,  H04N19/147 ,  H04N19/162

    CPC分类号： H04N19/137 ,  H04N19/147 ,  H04N19/162

    摘要： Systems and techniques are described for processing image data using a residual model that can be configured with an adjustable number of sampling steps. For example, a process can include obtaining a latent representation of an image and processing, using a decoder of a machine learning model, the latent representation of the image to generate an initial reconstructed image. The process can further include processing, using the residual model, the initial reconstructed image and noise data to predict a plurality of predictions of a residual over a number of sampling steps. The residual represents a difference between the image and the initial reconstructed image. The process can include obtaining, from the plurality of predictions of the residual, a final residual representing the difference between the image and the initial reconstructed image. The process can further include combining the initial reconstructed image and the residual to generate a final reconstructed image.

    VIDEO CODING USING OPTICAL FLOW AND RESIDUAL PREDICTORS
    5.
    发明公开
    VIDEO CODING USING OPTICAL FLOW AND RESIDUAL PREDICTORS 审中-公开

    公开(公告)号：US20240015318A1

    公开(公告)日：2024-01-11

    申请号：US17862217

    申请日：2022-07-11

    申请人： QUALCOMM Incorporated

    发明人： Reza POURREZA ,  Hoang Cong Minh LE ,  Auke Joris WIGGERS

    IPC分类号： H04N19/52 ,  H04N19/137 ,  H04N19/105 ,  H04N19/172

    CPC分类号： H04N19/52 ,  H04N19/137 ,  H04N19/105 ,  H04N19/172

    摘要： Systems and techniques are provided for coding video data based on an optical flow correction and a residual correction. For example, a decoding device can obtain a frame of encoded video data associated with an input frame, the frame of encoded video data including an optical flow correction and a residual correction. A predicted optical flow can be generated based on one or more reference frames and a reference optical flow. A corrected prediction frame can be generated based on the predicted optical flow and the optical flow correction. A predicted residual can be generated based on at least the corrected prediction frame and a first reference frame included in the one or more reference frames. The decoding device can generate a reconstructed input frame based on the corrected prediction frame, the predicted residual, and the residual correction.

    HYBRID REINFORCEMENT LEARNING FOR AUTONOMOUS DRIVING
    6.
    发明申请
    HYBRID REINFORCEMENT LEARNING FOR AUTONOMOUS DRIVING 审中-公开

    公开(公告)号：US20200150672A1

    公开(公告)日：2020-05-14

    申请号：US16683129

    申请日：2019-11-13

    申请人： QUALCOMM Incorporated

    发明人： Mohammad NAGHSHVAR ,  Ahmed Kamel SADEK ,  Auke Joris WIGGERS

    IPC分类号： G05D1/02 ,  G05B13/02 ,  G06N3/04 ,  G05D1/00

    摘要： A method includes determining a current state of an environment of an autonomous agent, such as a vehicle. The method also includes determining, via a first neural network, a set of actions based on the current state. The method further includes determining whether further analysis of the set of actions is desired. The method selects an action from the set of actions using a model-based solution based on a reward and a risk of the action when further analysis is desired. The method also includes selecting the action from the set of actions according to a metric when further analysis is not desired. The method controls the autonomous agent to perform the selected action.