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    High-fidelity generative image compression
    1.
    发明授权
    High-fidelity generative image compression 有权

    公开(公告)号：US12225239B2

    公开(公告)日：2025-02-11

    申请号：US18238068

    申请日：2023-08-25

    Applicant: Google LLC

    Inventor： George Dan Toderici ,  Fabian Julius Mentzer ,  Eirikur Thor Agustsson ,  Michael Tobias Tschannen

    IPC: G06V10/00 ,  G06N3/045 ,  G06N3/088 ,  H04N19/124 ,  H04N19/154 ,  H04N19/91

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network configured to receive a data item and to process the data item to output a compressed representation of the data item. In one aspect, a method includes, for each training data item: processing the data item using the encoder neural network to generate a latent representation of the training data item; processing the latent representation using a hyper-encoder neural network to determine a conditional entropy model; generating a compressed representation of the training data item; processing the compressed representation using a decoder neural network to generate a reconstruction of the training data item; processing the reconstruction of the training data item using a discriminator neural network to generate a discriminator network output; evaluating a first loss function; and determining an update to the current values of the encoder network parameters.

    Learned volumetric attribute compression using coordinate-based networks
    2.
    发明授权
    Learned volumetric attribute compression using coordinate-based networks 有权

    公开(公告)号：US11900525B2

    公开(公告)日：2024-02-13

    申请号：US17708628

    申请日：2022-03-30

    Applicant: Google LLC

    Inventor： Philip Andrew Chou ,  Berivan Isik ,  Sung Jin Hwang ,  Nicholas Milo Johnston ,  George Dan Toderici

    IPC: G06T15/08 ,  H04N19/176 ,  H04N19/46

    CPC classification number: G06T15/08 ,  H04N19/176 ,  H04N19/46

    Abstract: Example embodiments of the present disclosure relate to systems and methods for compressing attributes of volumetric and hypervolumetric datasets. An example system performs operations including obtaining a reference dataset comprising attributes indexed by a domain of multidimensional coordinates; subdividing the domain into a plurality of blocks respectively associated with a plurality of attribute subsets; inputting, to a local nonlinear operator, a latent representation for an attribute subset associated with at least one block of the plurality of blocks; obtaining, using the local nonlinear operator and based on the latent representation, an attribute representation of one or more attributes of the attribute subset; and updating the latent representation based on a comparison of the attribute representation and the reference dataset.

    LEARNING COMPRESSIBLE FEATURES
    5.
    发明公开
    LEARNING COMPRESSIBLE FEATURES 审中-公开

    公开(公告)号：US20230237332A1

    公开(公告)日：2023-07-27

    申请号：US18175125

    申请日：2023-02-27

    Applicant: GOOGLE LLC

    Inventor： Abhinav Shrivastava ,  Saurabh Singh ,  Johannes Ballé ,  Sami Ahmad Abu-El-Haija ,  Nicholas Milo Johnston ,  George Dan Toderici

    IPC: G06N3/08 ,  G06F17/15 ,  G06N3/063

    CPC classification number: G06N3/08 ,  G06F17/15 ,  G06F18/24 ,  G06N3/063 ,  G06N3/082

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving, by a neural network (NN), a dataset for generating features from the dataset. A first set of features is computed from the dataset using at least a feature layer of the NN. The first set of features i) is characterized by a measure of informativeness; and ii) is computed such that a size of the first set of features is compressible into a second set of features that is smaller in size than the first set of features and that has a same measure of informativeness as the measure of informativeness of the first set of features. The second set of features if generated from the first set of features using a compression method that compresses the first set of features to generate the second set of features.

    FEATURE-BASED VIDEO ANNOTATION
    6.
    发明申请
    FEATURE-BASED VIDEO ANNOTATION 有权

    公开(公告)号：US20220207873A1

    公开(公告)日：2022-06-30

    申请号：US17548859

    申请日：2021-12-13

    Applicant: Google LLC

    Inventor： Balakrishnan Varadarajan ,  George Dan Toderici ,  Apostol Natsev ,  Nitin Khandelwal ,  Sudheendra Vijayanarasimhan ,  Weilong Yang ,  Sanketh Shetty

    IPC: G06V20/40 ,  G06F16/783 ,  G06F16/78 ,  G06V10/42 ,  G06K9/62 ,  H04N5/265

    Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.

    Tiled image compression using neural networks
    7.
    发明授权
    Tiled image compression using neural networks 有权

    公开(公告)号：US11250595B2

    公开(公告)日：2022-02-15

    申请号：US16617484

    申请日：2018-05-29

    Applicant: GOOGLE LLC

    Inventor： Michele Covell ,  Damien Vincent ,  David Charles Minnen ,  Saurabh Singh ,  Sung Jin Hwang ,  Nicholas Johnston ,  Joel Eric Shor ,  George Dan Toderici

    IPC: G06T9/00 ,  G06N3/04 ,  G06N3/08 ,  G06T3/40 ,  G06T7/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for image compression and reconstruction. An image encoder system receives a request to generate an encoded representation of an input image that has been partitioned into a plurality of tiles and generates the encoded representation of the input image. To generate the encoded representation, the system processes a context for each tile using a spatial context prediction neural network that has been trained to process context for an input tile and generate an output tile that is a prediction of the input tile. The system determines a residual image between the particular tile and the output tile generated by the spatial context prediction neural network by process the context for the particular tile and generates a set of binary codes for the particular tile by encoding the residual image using an encoder neural network.

    Data compression by local entropy encoding
    8.
    发明授权
    Data compression by local entropy encoding 有权

    公开(公告)号：US11177823B2

    公开(公告)日：2021-11-16

    申请号：US15985340

    申请日：2018-05-21

    Applicant: Google LLC

    Inventor： David Charles Minnen ,  Michele Covell ,  Saurabh Singh ,  Sung Jin Hwang ,  George Dan Toderici

    IPC: H04N19/91 ,  H03M7/30 ,  H04N19/13 ,  H04N19/192 ,  G06F16/28 ,  G06F16/23 ,  G06N3/08 ,  G06N7/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing and decompressing data. In one aspect, an encoder neural network processes data to generate an output including a representation of the data as an ordered collection of code symbols. The ordered collection of code symbols is entropy encoded using one or more code symbol probability distributions. A compressed representation of the data is determined based on the entropy encoded representation of the collection of code symbols and data indicating the code symbol probability distributions used to entropy encode the collection of code symbols. In another aspect, a compressed representation of the data is decoded to determine the collection of code symbols representing the data. A reconstruction of the data is determined by processing the collection of code symbols by a decoder neural network.

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