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    Surgical micro-encoding of content
    5.
    发明授权
    Surgical micro-encoding of content 有权

    公开(公告)号：US12126879B2

    公开(公告)日：2024-10-22

    申请号：US17861063

    申请日：2022-07-08

    Applicant: Disney Enterprises, Inc.

    Inventor： Scott Labrozzi ,  William B. May, Jr.

    IPC: H04N21/845 ,  H04N21/2383

    CPC classification number: H04N21/8455 ,  H04N21/2383

    Abstract: A system includes a computing platform having processing hardware, and a memory storing software code. The software code is executed to receive content having a sequence of content segments, and marker data identifying a location within the sequence, identify, using the content and the marker data, segment boundaries of a content segment containing the location, determine, using the location and the segment boundaries, whether the location is situated within a predetermined interval of one of the segment boundaries, and re-encode a subsection of the sequence to produce a new segment boundary at the location. When the location is not situated within the predetermined interval, the subsection of the sequence includes the content segment containing the location. When the location is situated within the predetermined interval, the subsection of the sequence includes the content segment containing the location and a content segment adjoining the content segment containing the location.

    Machine Learning Model-Based Video Compression
    7.
    发明申请
    Machine Learning Model-Based Video Compression 有权

    公开(公告)号：US20220329876A1

    公开(公告)日：2022-10-13

    申请号：US17704692

    申请日：2022-03-25

    Applicant: Disney Enterprises, Inc. ,  ETH Zürich (EIDGENÖSSISCHE TECHNISCHE HOCHSCHULE ZÜRICH)

    Inventor： Abdelaziz Djelouah ,  Leonhard Markus Helminger ,  Roberto Gerson de Albuquerque Azevedo ,  Scott Labrozzi ,  Christopher Richard Schroers ,  Yuanyi Xue

    IPC: H04N19/89 ,  G06N3/08

    Abstract: A system processing hard e executes a machine learning (ML) model-based video compression encoder to receive uncompressed video content and corresponding motion compensated video content, compare the uncompressed and motion compensated video content to identify an image space residual, transform the image space residual to a latent space representation of the uncompressed video content, and transform, using a trained image compression ML model, the motion compensated video content to a latent space representation of the motion compensated video content. The ML model-based video compression encoder further encodes the latent space representation of the image space residual to produce an encoded latent residual, encodes, using the trained image compression ML model, the latent space representation of the motion compensated video content to produce an encoded latent video content, and generates, using the encoded latent residual and the encoded latent video content, a compressed video content corresponding to the uncompressed video content.

    SYSTEMS AND METHODS FOR DISTORTION REMOVAL AT MULTIPLE QUALITY LEVELS
    9.
    发明申请
    SYSTEMS AND METHODS FOR DISTORTION REMOVAL AT MULTIPLE QUALITY LEVELS 审中-公开

    公开(公告)号：US20190333190A1

    公开(公告)日：2019-10-31

    申请号：US16167388

    申请日：2018-10-22

    Applicant: Disney Enterprises, Inc.

    Inventor： Christopher Schroers ,  Mauro Bamert ,  Erika Doggett ,  Jared McPhillen ,  Scott Labrozzi ,  Romann Weber

    IPC: G06T5/00 ,  G06N3/08 ,  G06T5/20

    Abstract: Systems and methods for distortion removal at multiple quality levels are disclosed. In one embodiment, a method may include receiving training content. The training content may include original content, reconstructed content, and training distortion quality levels corresponding to the reconstructed content. The reconstructed content may be derived from distorted original content. The method may also include training distortion quality levels corresponding to the reconstructed content. The method may further include receiving an initial distortion removal model. The method may include generating a conditioned distortion removal model by training the initial distortion removal model using the training content. The method may further include storing the conditioned distortion removal model.

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