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公开(公告)号：US20190356330A1
公开(公告)日：2019-11-21
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
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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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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公开(公告)号：US20240223817A1
公开(公告)日：2024-07-04
申请号：US18563734
申请日：2022-07-05
Applicant: Google LLC
Inventor： George Dan Toderici , Eirikur Thor Agustsson , Fabian Julius Mentzer , David Charles Minnen , Johannes Balle , Nicholas Johnston
IPC: H04N19/91 , G06T3/18 , G06T5/70 , H04N19/124 , H04N19/137
CPC classification number: H04N19/91 , G06T3/18 , G06T5/70 , H04N19/124 , H04N19/137 , G06T2207/20084
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing video data. In one aspect, a method comprises: receiving a video sequence of frames; generating, using a flow prediction network, an optical flow between two sequential frames, wherein the two sequential frames comprise a first frame and a second frame that is subsequent the first frame; generating from the optical flow, using a first autoencoder neural network: a predicted optical flow between the first frame and the second frame; and warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation to obtain an initial predicted reconstruction of the second frame.
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公开(公告)号：US12026925B2
公开(公告)日：2024-07-02
申请号：US18461292
申请日：2023-09-05
Applicant: Google LLC
Inventor： David Charles Minnen , Saurabh Singh
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for channel-wise autoregressive entropy models. In one aspect, a method includes processing data using a first encoder neural network to generate a latent representation of the data. The latent representation of data is processed by a quantizer and a second encoder neural network to generate a quantized latent representation of data and a latent representation of an entropy model. The latent representation of data is further processed into a plurality of slices of quantized latent representations of data wherein the slices are arranged in an ordinal sequence. A hyperprior processing network generates a hyperprior parameters and a compressed representation of the hyperprior parameters. For each slice, a corresponding compressed representation is generated using a corresponding slice processing network wherein a combination of the compressed representations form a compressed representation of the data.
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公开(公告)号：US11783511B2
公开(公告)日：2023-10-10
申请号：US18088283
申请日：2022-12-23
Applicant: Google LLC
Inventor： David Charles Minnen , Saurabh Singh
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for channel-wise autoregressive entropy models. In one aspect, a method includes processing data using a first encoder neural network to generate a latent representation of the data. The latent representation of data is processed by a quantizer and a second encoder neural network to generate a quantized latent representation of data and a latent representation of an entropy model. The latent representation of data is further processed into a plurality of slices of quantized latent representations of data wherein the slices are arranged in an ordinal sequence. A hyperprior processing network generates a hyperprior parameters and a compressed representation of the hyperprior parameters. For each slice, a corresponding compressed representation is generated using a corresponding slice processing network wherein a combination of the compressed representations form a compressed representation of the data.
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Patent Agency Ranking
公开(公告)号：US20230206512A1
公开(公告)日：2023-06-29
申请号：US18088283
申请日：2022-12-23
Applicant: Google LLC
Inventor： David Charles Minnen , Saurabh Singh
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for channel-wise autoregressive entropy models. In one aspect, a method includes processing data using a first encoder neural network to generate a latent representation of the data. The latent representation of data is processed by a quantizer and a second encoder neural network to generate a quantized latent representation of data and a latent representation of an entropy model. The latent representation of data is further processed into a plurality of slices of quantized latent representations of data wherein the slices are arranged in an ordinal sequence. A hyperprior processing network generates a hyperprior parameters and a compressed representation of the hyperprior parameters. For each slice, a corresponding compressed representation is generated using a corresponding slice processing network wherein a combination of the compressed representations form a compressed representation of the data.
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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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