公开(公告)号：US20200027247A1

公开(公告)日：2020-01-23

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 ,  G06T3/40 ,  G06N3/08 ,  G06N20/00 ,  G06F17/18

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.

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

IPC: G06T9/00 ,  G06K9/66 ,  G06K9/62

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.