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公开(公告)号：US10192327B1
公开(公告)日：2019-01-29
申请号：US15424711
申请日：2017-02-03
Applicant: Google LLC
Inventor： George Dan Toderici , Sean O'Malley , Rahul Sukthankar , Sung Jin Hwang , Damien Vincent , Nicholas Johnston , David Charles Minnen , Joel Shor , Michele Covell
Abstract: Methods, and systems, including computer programs encoded on computer storage media for compressing data items with variable compression rate. A system includes an encoder sub-network configured to receive a system input image and to generate an encoded representation of the system input image, the encoder sub-network including a first stack of neural network layers including one or more LSTM neural network layers and one or more non-LSTM neural network layers, the first stack configured to, at each of a plurality of time steps, receive an input image for the time step that is derived from the system input image and generate a corresponding first stack output, and a binarizing neural network layer configured to receive a first stack output as input and generate a corresponding binarized output.
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Patent Agency Ranking
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.
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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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公开(公告)号：US20220138991A1
公开(公告)日：2022-05-05
申请号：US17578794
申请日：2022-01-19
Applicant: Google LLC
Inventor： David Charles Minnen , Saurabh Singh , Johannes Balle , Troy Chinen , Sung Jin Hwang , Nicholas Johnston , George Dan Toderici
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.
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公开(公告)号：US20210335017A1
公开(公告)日：2021-10-28
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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公开(公告)号：US10713818B1
公开(公告)日：2020-07-14
申请号：US16259207
申请日：2019-01-28
Applicant: Google LLC
Inventor： George Dan Toderici , Sean O'Malley , Rahul Sukthankar , Sung Jin Hwang , Damien Vincent , Nicholas Johnston , David Charles Minnen , Joel Shor , Michele Covell
Abstract: Methods, and systems, including computer programs encoded on computer storage media for compressing data items with variable compression rate. A system includes an encoder sub-network configured to receive a system input image and to generate an encoded representation of the system input image, the encoder sub-network including a first stack of neural network layers including one or more LSTM neural network layers and one or more non-LSTM neural network layers, the first stack configured to, at each of a plurality of time steps, receive an input image for the time step that is derived from the system input image and generate a corresponding first stack output, and a binarizing neural network layer configured to receive a first stack output as input and generate a corresponding binarized output.
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