Patents by Inventor Oren Rippel

Oren Rippel has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210295164
    Abstract: A deep learning based compression (DLBC) system applies trained models to compress binary code of an input image to a target codelength. For a set of binary codes representing the quantized coefficents of an input image, the DLBC system applies a first model that is trained to predict feature probabilities based on the context of each bit of the binary codes. The DLBC system compresses the binary code via adaptive arithmetic coding based on the determined probability of each bit. The compressed binary code represents a balance between a reconstruction quality of a reconstruction of the input image and a target compression ratio of the compressed binary code.
    Type: Application
    Filed: June 9, 2021
    Publication date: September 23, 2021
    Inventors: Oren Rippel, Lubomir Bourdev
  • Patent number: 11100394
    Abstract: A deep learning based compression (DLBC) system applies trained models to compress binary code of an input image to a target codelength. For a set of binary codes representing the quantized coefficents of an input image, the DLBC system applies a first model that is trained to predict feature probabilities based on the context of each bit of the binary codes. The DLBC system compresses the binary code via adaptive arithmetic coding based on the determined probability of each bit. The compressed binary code represents a balance between a reconstruction quality of a reconstruction of the input image and a target compression ratio of the compressed binary code.
    Type: Grant
    Filed: July 1, 2020
    Date of Patent: August 24, 2021
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Lubomir Bourdev
  • Patent number: 11062211
    Abstract: A deep learning based compression (DLBC) system applies trained models to compress binary code of an input image to a target codelength. For a set of binary codes representing the quantized coefficents of an input image, the DLBC system applies a first model that is trained to predict feature probabilities based on the context of each bit of the binary codes. The DLBC system compresses the binary code via adaptive arithmetic coding based on the determined probability of each bit. The compressed binary code represents a balance between a reconstruction quality of a reconstruction of the input image and a target compression ratio of the compressed binary code.
    Type: Grant
    Filed: July 1, 2020
    Date of Patent: July 13, 2021
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Lubomir Bourdev
  • Patent number: 10977553
    Abstract: A deep learning based compression (DLBC) system generates a progressive representation of the encoded input image such that a client device that requires the encoded input image at a particular target bitrate can readily be transmitted the appropriately encoded data. More specifically, the DLBC system computes a representation that includes channels and bitplanes that are ordered based on importance. For a given target rate, the DLBC system truncates the representation according to a trained zero mask to generate the progressive representation. Transmitting a first portion of the progressive representation enables a client device with the lowest target bitrate to appropriately playback the content. Each subsequent portion of the progressive representation allows the client device to playback the content with improved quality.
    Type: Grant
    Filed: May 8, 2019
    Date of Patent: April 13, 2021
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Lubomir Bourdev
  • Patent number: 10860929
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Grant
    Filed: May 11, 2020
    Date of Patent: December 8, 2020
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Publication number: 20200334535
    Abstract: A deep learning based compression (DLBC) system applies trained models to compress binary code of an input image to a target codelength. For a set of binary codes representing the quantized coefficents of an input image, the DLBC system applies a first model that is trained to predict feature probabilities based on the context of each bit of the binary codes. The DLBC system compresses the binary code via adaptive arithmetic coding based on the determined probability of each bit. The compressed binary code represents a balance between a reconstruction quality of a reconstruction of the input image and a target compression ratio of the compressed binary code.
    Type: Application
    Filed: July 1, 2020
    Publication date: October 22, 2020
    Inventors: Oren Rippel, Lubomir Bourdev
  • Publication number: 20200334534
    Abstract: A deep learning based compression (DLBC) system applies trained models to compress binary code of an input image to a target codelength. For a set of binary codes representing the quantized coefficents of an input image, the DLBC system applies a first model that is trained to predict feature probabilities based on the context of each bit of the binary codes. The DLBC system compresses the binary code via adaptive arithmetic coding based on the determined probability of each bit. The compressed binary code represents a balance between a reconstruction quality of a reconstruction of the input image and a target compression ratio of the compressed binary code.
    Type: Application
    Filed: July 1, 2020
    Publication date: October 22, 2020
    Inventors: Oren Rippel, Lubomir Bourdev
  • Publication number: 20200272903
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Application
    Filed: May 11, 2020
    Publication date: August 27, 2020
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Patent number: 10748062
    Abstract: A deep learning based compression (DLBC) system applies trained models to compress binary code of an input image to a target codelength. For a set of binary codes representing the quantized coefficents of an input image, the DLBC system applies a first model that is trained to predict feature probabilities based on the context of each bit of the binary codes. The DLBC system compresses the binary code via adaptive arithmetic coding based on the determined probability of each bit. The compressed binary code represents a balance between a reconstruction quality of a reconstruction of the input image and a target compression ratio of the compressed binary code.
    Type: Grant
    Filed: February 22, 2017
    Date of Patent: August 18, 2020
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Lubomir Bourdev
  • Patent number: 10685282
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Grant
    Filed: November 7, 2018
    Date of Patent: June 16, 2020
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Patent number: 10594338
    Abstract: A compression system includes an encoder and a decoder. The encoder can be deployed by a sender system to encode a tensor for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode and reconstruct the encoded tensor. The encoder receives a tensor for compression. The encoder also receives a quantization mask and probability data associated with the tensor. Each element of the tensor is quantized using an alphabet size allocated to that element by the quantization mask data. The encoder compresses the tensor by entropy coding each element using the probability data and alphabet size associated with the element. The decoder receives the quantization mask data, the probability data, and the compressed tensor data. The quantization mask and probabilities are used to entropy decode and subsequently reconstruct the tensor.
    Type: Grant
    Filed: March 18, 2019
    Date of Patent: March 17, 2020
    Assignee: WaveOne Inc.
    Inventors: Carissa Lew, Steven Branson, Oren Rippel, Sanjay Nair, Alexander Grant Anderson, Lubomir Bourdev
  • Patent number: 10565499
    Abstract: An enhanced encoder system generates residual bitstreams representing additional image information that can be used by an image enhancement system to improve a low quality image. The enhanced encoder system upsamples a low quality image and compares the upsampled image to a true high quality image to determine image inaccuracies that arise due to the upsampling process. The enhanced encoder system encodes the information describing the image inaccuracies using a trained encoder model as the residual bitstream. The image enhancement system upsamples the same low quality image to obtain a prediction of a high quality image that can include image inaccuracies. Given the residual bitstream, the image enhancement system decodes the residual bitstream using a trained decoder model and uses the additional image information to improve the predicted high quality image. The image enhancement system can provide an improved, high quality image for display.
    Type: Grant
    Filed: December 15, 2017
    Date of Patent: February 18, 2020
    Assignee: WaveOne Inc.
    Inventors: Lubomir Bourdev, Carissa Lew, Sanjay Nair, Oren Rippel
  • Publication number: 20200036995
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Application
    Filed: November 7, 2018
    Publication date: January 30, 2020
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Publication number: 20200034709
    Abstract: An autoencoder is configured to encode content at different quality levels. The autoencoder includes an encoding system and a decoding system with neural network layers forming an encoder network and a decoder network. The encoder network and decoder network are configured to include branching paths through the networks that include different subnetworks. During deployment, content is provided to the encoding system with a quality signal indicating a quality at which the content can be reconstructed. The quality signal determines which of the paths through the encoder network are activated for encoding the content into one or more tensors, which are compressed into a bitstream and later used by the decoding system to reconstruct the content. The autoencoder is trained by randomly or systematically selecting different combinations of tensors to use to encode content and backpropagating error values from loss functions through the network paths associated with the selected tensors.
    Type: Application
    Filed: July 22, 2019
    Publication date: January 30, 2020
    Inventors: Oren Rippel, Lubomir Bourdev
  • Patent number: 10402722
    Abstract: A compression system trains a machine-learned encoder and decoder. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder receives content and generates a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder receives a tensor and generates a reconstructed version of the content. In one embodiment, the compression system trains one or more encoding components such that the encoder can adaptively encode different degrees of information for regions in the content that are associated with characteristic objects, such as human faces, texts, or buildings.
    Type: Grant
    Filed: December 15, 2017
    Date of Patent: September 3, 2019
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Lubomir Bourdev, Carissa Lew, Sanjay Nair
  • Publication number: 20190266490
    Abstract: A deep learning based compression (DLBC) system generates a progressive representation of the encoded input image such that a client device that requires the encoded input image at a particular target bitrate can readily be transmitted the appropriately encoded data. More specifically, the DLBC system computes a representation that includes channels and bitplanes that are ordered based on importance. For a given target rate, the DLBC system truncates the representation according to a trained zero mask to generate the progressive representation. Transmitting a first portion of the progressive representation enables a client device with the lowest target bitrate to appropriately playback the content. Each subsequent portion of the progressive representation allows the client device to playback the content with improved quality.
    Type: Application
    Filed: May 8, 2019
    Publication date: August 29, 2019
    Inventors: Oren Rippel, Lubomir Bourdev
  • Patent number: 10332001
    Abstract: A deep learning based compression (DLBC) system generates a progressive representation of the encoded input image such that a client device that requires the encoded input image at a particular target bitrate can readily be transmitted the appropriately encoded data. More specifically, the DLBC system computes a representation that includes channels and bitplanes that are ordered based on importance. For a given target rate, the DLBC system truncates the representation according to a trained zero mask to generate the progressive representation. Transmitting a first portion of the progressive representation enables a client device with the lowest target bitrate to appropriately playback the content. Each subsequent portion of the progressive representation allows the client device to playback the content with improved quality.
    Type: Grant
    Filed: February 22, 2017
    Date of Patent: June 25, 2019
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Lubomir Bourdev
  • Publication number: 20180176570
    Abstract: A deep learning based compression (DLBC) system trains multiple models that, when deployed, generates a compressed binary encoding of an input image that achieves a reconstruction quality and a target compression ratio. The applied models effectively identifies structures of an input image, quantizes the input image to a target bit precision, and compresses the binary code of the input image via adaptive arithmetic coding to a target codelength. During training, the DLBC system reconstructs the input image from the compressed binary encoding and determines the loss in quality from the encoding process. Thus, the models can be continually trained to, when applied to an input image, minimize the loss in reconstruction quality that arises due to the encoding process while also achieving the target compression ratio.
    Type: Application
    Filed: February 22, 2017
    Publication date: June 21, 2018
    Inventors: Oren Rippel, Lubomir Bourdev
  • Publication number: 20180174047
    Abstract: A machine learning (ML) task system trains a neural network model that learns a compressed representation of acquired data and performs a ML task using the compressed representation. The neural network model is trained to generate a compressed representation that balances the objectives of achieving a target codelength and achieving a high accuracy of the output of the performed ML task. During deployment, an encoder portion and a task portion of the neural network model are separately deployed. A first system acquires data, applies the encoder portion to generate a compressed representation, performs an encoding process to generate compressed codes, and transmits the compressed codes. A second system regenerates the compressed representation from the compressed codes and applies the task model to determine the output of a ML task.
    Type: Application
    Filed: December 15, 2017
    Publication date: June 21, 2018
    Inventors: Lubomir Bourdev, Carissa Lew, Sanjay Nair, Oren Rippel
  • Publication number: 20180174275
    Abstract: An enhanced encoder system generates residual bitstreams representing additional image information that can be used by an image enhancement system to improve a low quality image. The enhanced encoder system upsamples a low quality image and compares the upsampled image to a true high quality image to determine image inaccuracies that arise due to the upsampling process. The enhanced encoder system encodes the information describing the image inaccuracies using a trained encoder model as the residual bitstream. The image enhancement system upsamples the same low quality image to obtain a prediction of a high quality image that can include image inaccuracies. Given the residual bitstream, the image enhancement system decodes the residual bitstream using a trained decoder model and uses the additional image information to improve the predicted high quality image. The image enhancement system can provide an improved, high quality image for display.
    Type: Application
    Filed: December 15, 2017
    Publication date: June 21, 2018
    Inventors: Lubomir Bourdev, Carissa Lew, Sanjay Nair, Oren Rippel