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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
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.
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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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Patent Agency Ranking
公开(公告)号：US11019366B2
公开(公告)日：2021-05-25
申请号：US16413992
申请日：2019-05-16
Applicant: GOOGLE LLC
Inventor： David Marwood , Michele Covell , Shumeet Baluja , Nicholas Milo Johnston , Pascal Massimino
IPC: G06T9/00 , H04N19/90 , H04N19/186 , H04N19/176
Abstract: An encoder system can include a pixel grid generator to receive an image having a first dimension, generate a grid having a second dimension, add a plurality of points to positions on the grid, and map a plurality of pixels of the image to the plurality of points. The encoder system can include a color module to assign a color to each of the plurality of points using a color table, a triangulation module to generate a plurality of vertices based on the plurality of points and triangulate the grid using the vertices, and a compression module to compress the vertices as a set of compressed vertex positions and a set of vertex colors.
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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.
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公开(公告)号：US20190356931A1
公开(公告)日：2019-11-21
申请号：US16413992
申请日：2019-05-16
Applicant: GOOGLE LLC
Inventor： David Marwood , Michele Covell , Shumeet Baluja , Nicholas Milo Johnston , Pascal Massimino
IPC: H04N19/90 , H04N19/186 , H04N19/176 , G06T9/00
Abstract: An encoder system can include a pixel grid generator to receive an image having a first dimension, generate a grid having a second dimension, add a plurality of points to positions on the grid, and map a plurality of pixels of the image to the plurality of points. The encoder system can include a color module to assign a color to each of the plurality of points using a color table, a triangulation module to generate a plurality of vertices based on the plurality of points and triangulate the grid using the vertices, and a compression module to compress the vertices as a set of compressed vertex positions and a set of vertex colors.
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公开(公告)号：US12033077B2
公开(公告)日：2024-07-09
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
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.
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公开(公告)号：US20240144583A1
公开(公告)日：2024-05-02
申请号：US18398009
申请日：2023-12-27
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.
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