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    Method For Constructing A Perceptual Metric For Judging Video Quality
    11.
    发明申请
    Method For Constructing A Perceptual Metric For Judging Video Quality 有权

    公开(公告)号：US20230099526A1

    公开(公告)日：2023-03-30

    申请号：US17486359

    申请日：2021-09-27

    Applicant: Google LLC

    Inventor： Troy Chinen ,  Alex Sukhanov ,  Eirikur Thor Agustsson ,  George Dan Toderici

    IPC: G06T7/00 ,  G06N20/00 ,  G06K9/62 ,  H04N19/23

    Abstract: Example aspects of the present disclosure are directed to a computer-implemented method for determining a perceptual quality of a subject video content item. The method can include inputting a subject frame set from the subject video content item into a first machine-learned model. The method can also include generating, using the first machine-learned model, a feature based at least in part on the subject frame set. The method can also include outputting, using a second machine-learned model, a score indicating the perceptual quality of the subject video content item based at least in part on the feature.

    Learning compressible features
    12.
    发明授权
    Learning compressible features 有权

    公开(公告)号：US11610124B2

    公开(公告)日：2023-03-21

    申请号：US16666689

    申请日：2019-10-29

    Applicant: Google LLC

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

    IPC: G06K9/00 ,  G06N3/082 ,  G06F17/15 ,  G06K9/62 ,  G06N3/063

    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.

    Data compression using conditional entropy models
    13.
    发明授权
    Data compression using conditional entropy models 有权

    公开(公告)号：US11257254B2

    公开(公告)日：2022-02-22

    申请号：US16515586

    申请日：2019-07-18

    Applicant: Google LLC

    Inventor： David Charles Minnen ,  Saurabh Singh ,  Johannes Balle ,  Troy Chinen ,  Sung Jin Hwang ,  Nicholas Johnston ,  George Dan Toderici

    IPC: G06T9/00 ,  G06N20/00 ,  G06F17/18 ,  G06N3/08 ,  G06T3/40

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing and decompressing data. In one aspect, a method comprises: processing data using an encoder neural network to generate a latent representation of the data; processing the latent representation of the data using a hyper-encoder neural network to generate a latent representation of an entropy model; generating an entropy encoded representation of the latent representation of the entropy model; generating an entropy encoded representation of the latent representation of the data using the latent representation of the entropy model; and determining a compressed representation of the data from the entropy encoded representations of: (i) the latent representation of the data and (ii) the latent representation of the entropy model used to entropy encode the latent representation of the data.

    Classifying videos using neural networks
    14.
    发明授权
    Classifying videos using neural networks 有权

    公开(公告)号：US11074454B1

    公开(公告)日：2021-07-27

    申请号：US16410863

    申请日：2019-05-13

    Applicant: Google LLC

    Inventor： Sudheendra Vijayanarasimhan ,  George Dan Toderici ,  Yue Hei Ng ,  Matthew John Hausknecht ,  Oriol Vinyals ,  Rajat Monga

    IPC: G06K9/62 ,  G06K9/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying videos using neural networks. One of the methods includes obtaining a temporal sequence of video frames, wherein the temporal sequence comprises a respective video frame from a particular video at each of a plurality time steps; for each time step of the plurality of time steps: processing the video frame at the time step using a convolutional neural network to generate features of the video frame; and processing the features of the video frame using an LSTM neural network to generate a set of label scores for the time step and classifying the video as relating to one or more of the topics represented by labels in the set of labels from the label scores for each of the plurality of time steps.

    HIGH-FIDELITY GENERATIVE IMAGE COMPRESSION
    18.
    发明公开
    HIGH-FIDELITY GENERATIVE IMAGE COMPRESSION 审中-公开

    公开(公告)号：US20240107079A1

    公开(公告)日：2024-03-28

    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: H04N19/91 ,  G06N3/045 ,  G06N3/088 ,  H04N19/124 ,  H04N19/154

    CPC classification number: H04N19/91 ,  G06N3/045 ,  G06N3/088 ,  H04N19/124 ,  H04N19/154

    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
    19.
    发明公开
    Learned Volumetric Attribute Compression Using Coordinate-Based Networks 审中-公开

    公开(公告)号：US20230260197A1

    公开(公告)日：2023-08-17

    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 ,  H04N19/46 ,  H04N19/176

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

    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.

    Stop code tolerant image compression neural networks
    20.
    发明授权
    Stop code tolerant image compression neural networks 有权

    公开(公告)号：US11354822B2

    公开(公告)日：2022-06-07

    申请号：US16610063

    申请日：2018-05-16

    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

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for image compression and reconstruction. A request to generate an encoded representation of an input image is received. The encoded representation of the input image is then generated. The encoded representation includes a respective set of binary codes at each iteration. Generating the set of binary codes for the iteration from an initial set of binary includes: for any tiles that have already been masked off during any previous iteration, masking off the tile. For any tiles that have not yet been masked off during any of the previous iterations, a determination is made as to whether a reconstruction error of the tile when reconstructed from binary codes at the previous iterations satisfies an error threshold. When the reconstruction quality satisfies the error threshold, the tile is masked off.

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