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    Linear light scaling service for non-linear light pixel values
    7.
    发明授权
    Linear light scaling service for non-linear light pixel values 有权

    公开(公告)号：US11218743B1

    公开(公告)日：2022-01-04

    申请号：US16916721

    申请日：2020-06-30

    Applicant: Amazon Technologies, Inc.

    Inventor： Charles Benjamin Waggoner ,  Hai Wei

    IPC: H04N21/2343 ,  H04N21/2662 ,  H04N21/239 ,  H04N11/20

    Abstract: Techniques for a fast approximation of linear light scaling for inputs of non-linear light values are described. As one example, a computer-implemented method includes receiving a request to downscale a plurality of pixels of a single frame of a video file performing, in response to the request to downscale, a lookup in a lookup table for a first input of a first non-linear light value of luminance for a first pixel of the plurality of pixels and a second input of a second non-linear light value of luminance for a second pixel of the plurality of pixels to generate an output of a third single non-linear light value of luminance for a linear light scaling for the first pixel and the second pixel, generating a scaled frame based at least in part on the third single non-linear light value of luminance for the linear light scaling, receiving a request for a manifest for the video file from a client device, generating the manifest for the client device that identifies a scaled video representation that comprises the scaled frame, and sending the manifest to the client device.

    Content-adaptive video sampling for cost-effective quality monitoring
    9.
    发明授权
    Content-adaptive video sampling for cost-effective quality monitoring 有权

    公开(公告)号：US11445168B1

    公开(公告)日：2022-09-13

    申请号：US17039349

    申请日：2020-09-30

    Applicant: Amazon Technologies, Inc.

    Inventor： Hai Wei ,  Charles Benjamin Waggoner ,  Zongyi Liu ,  Srinivas Rajagopalan ,  Lei Li

    IPC: H04N17/00 ,  G06K9/62 ,  G06T7/00 ,  G06V20/40

    Abstract: Techniques for content-adaptive video sampling for automated video quality monitoring are described. As one example, a computer-implemented method includes receiving a request to train a machine learning model on a training video file comprising at least one labeled defect, performing an encode on the training video file to generate one or more compression features for each compressed frame of the training video file, training the machine learning model to identify a proper subset of candidate defect frames of the training video file based at least in part on the one or more compression features for each compressed frame of the training video file and the at least one labeled defect, receiving an inference request for an input video file, performing an encode on the input video file to generate one or more compression features for each compressed frame of the input video file, generating, by the machine learning model, a proper subset of candidate defect frames of the input video file based at least in part on the one or more compression features for each compressed frame of the input video file, and determining a defect in the input video file based at least in part on the proper subset of candidate defect frames of the input video file.

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