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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
申请人： GOOGLE LLC
发明人： Michele Covell , Damien Vincent , David Charles Minnen , Saurabh Singh , Sung Jin Hwang , Nicholas Johnston , Joel Eric Shor , George Dan Toderici
IPC分类号： G06T9/00
摘要： 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
申请人： Google LLC
发明人： David Charles Minnen , Saurabh Singh , Johannes Balle , Troy Chinen , Sung Jin Hwang , Nicholas Johnston , George Dan Toderici
摘要： 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
申请人： GOOGLE LLC
发明人： Michele Covell , Damien Vincent , David Charles Minnen , Saurabh Singh , Sung Jin Hwang , Nicholas Johnston , Joel Eric Shor , George Dan Toderici
IPC分类号： G06T9/00
摘要： 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：US20200311548A1
公开(公告)日：2020-10-01
申请号：US16666689
申请日：2019-10-29
申请人： Google LLC
发明人： Abhinav Shrivastava , Saurabh Singh , Johannes Balle , Sami Ahmad Abu-El-Haija , Nicholas Johnston , George Dan Toderici
摘要： 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.
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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
申请人： Google LLC
发明人： George Dan Toderici , Sean O'Malley , Rahul Sukthankar , Sung Jin Hwang , Damien Vincent , Nicholas Johnston , David Charles Minnen , Joel Shor , Michele Covell
摘要： 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：US20240078712A1
公开(公告)日：2024-03-07
申请号：US18306771
申请日：2023-04-25
申请人： Google LLC
发明人： David Charles Minnen , Saurabh Singh , Johannes Balle , Troy Chinen , Sung Jin Hwang , Nicholas Johnston , George Dan Toderici
摘要： 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：US11488016B2
公开(公告)日：2022-11-01
申请号：US16751175
申请日：2020-01-23
申请人： Google LLC
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing a network input using a neural network to generate a network output for the network input. One of the methods includes maintaining, for each of the plurality of neural network layers, a respective look-up table that maps each possible combination of a quantized input index and a quantized weight index to a multiplication result; and generating a network output from a network input, comprising, for each of the neural network layers: receiving data specifying a quantized input to the neural network layer, the quantized input comprising a plurality of quantized input values; and generating a layer output for the neural network layer from the quantized input to the neural network layer using the respective look-up table for the neural network layer.
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公开(公告)号：US20210358180A1
公开(公告)日：2021-11-18
申请号：US17274596
申请日：2019-09-18
申请人： Google LLC
发明人： Nicholas Johnston , Johannes Balle
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reliably performing data compression and data decompression across a wide variety of hardware and software platforms by using integer neural networks. In one aspect, there is provided a method for entropy encoding data which defines a sequence comprising a plurality of components, the method comprising: for each component of the plurality of components: processing an input comprising: (i) a respective integer representation of each of one or more components of the data which precede the component in the sequence, (ii) an integer representation of one or more respective latent variables characterizing the data, or (iii) both, using an integer neural network to generate data defining a probability distribution over the predetermined set of possible code symbols for the component of the data.
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公开(公告)号：US20200027247A1
公开(公告)日：2020-01-23
申请号：US16515586
申请日：2019-07-18
申请人： Google LLC
发明人： David Charles Minnen , Saurabh Singh , Johannes Balle , Troy Chinen , Sung Jin Hwang , Nicholas Johnston , George Dan Toderici
摘要： 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：US10192327B1
公开(公告)日：2019-01-29
申请号：US15424711
申请日：2017-02-03
申请人： Google LLC
发明人： George Dan Toderici , Sean O'Malley , Rahul Sukthankar , Sung Jin Hwang , Damien Vincent , Nicholas Johnston , David Charles Minnen , Joel Shor , Michele Covell
摘要： 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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