Qualcomm Incorporated

Ties Jehan Van Rozendaal

Taco Sebastiaan Cohen

Amirhossein Habibian

Guillaume Konrad Sautiere

Yunfan Zhang

Johann Hinrich Brehmer

Yang Yang

Amir Said

Reza Pourreza

Abhijit Khobare

Auke Joris Wiggers

Liang Zhang

Markus Nagel

Muralidhar Reddy Akula

Krishna Buska

Tushar Singhal

Adam Waldemar Golinski

Iris Anne Marie Huijben

Frank Steven Mayer

Anjuman Raha

An example computing device may include memory and one or more processors. The one or more processors may be configured to parallel entropy decode encoded video data from a received bitstream to generate entropy decoded data. The one or more processors may be configured to predict a motion vector based on the entropy decoded data. The one or more processors may be configured to decode a motion vector residual from the entropy decoded data. The one or more processors may be configured to add the motion vector residual and motion vector. The one or more processors may be configured to warp previous reconstructed video data with an overlapped block-based warp function using the motion vector to generate predicted current video data. The one or more processors may be configured to sum the predicted current video data with a residual block to generate current reconstructed video data.

Efficient warping-based neural video codec

Techniques are described for compressing data using machine learning systems. An example process can include receiving input data for compression by a neural network compression system. The process can include determining, based on the input data, a set of updated model parameters for the neural network compression system, wherein the set of updated model parameters is selected from a subspace of model parameters. The process can include generating at least one bitstream including a compressed version of the input data and a compressed version of one or more subspace coordinates that correspond to the set of updated model parameters. The process can include outputting the at least one bitstream for transmission to a receiver.

Instance-adaptive image and video compression in a network parameter subspace using machine learning systems

Techniques are described for compressing and decompressing data using machine learning systems. An example process can include receiving a plurality of images for compression by a neural network compression system. The process can include determining, based on a first image from the plurality of images, a first plurality of weight values associated with a first model of the neural network compression system. The process can include generating a first bitstream comprising a compressed version of the first plurality of weight values. The process can include outputting the first bitstream for transmission to a receiver.

Implicit image and video compression using machine learning systems

Certain aspects of the present disclosure are directed to methods and apparatus for compressing video content using deep generative models. One example method generally includes receiving video content for compression. The received video content is generally encoded into a latent code space through an encoder, which may be implemented by a first artificial neural network. A compressed version of the encoded video content is generally generated through a trained probabilistic model, which may be implemented by a second artificial neural network, and output for transmission.

Video compression using deep generative models

Techniques are described for compressing data using machine learning systems and tuning machine learning systems for compressing the data. An example process can include receiving, by a neural network compression system (e.g., trained on a training dataset), input data for compression by the neural network compression system. The process can include determining a set of updates for the neural network compression system, the set of updates including updated model parameters tuned using the input data. The process can include generating, by the neural network compression system using a latent prior, a first bitstream including a compressed version of the input data. The process can further include generating, by the neural network compression system using the latent prior and a model prior, a second bitstream including a compressed version of the updated model parameters. The process can include outputting the first bitstream and the second bitstream for transmission to a receiver.

Instance-adaptive image and video compression using machine learning systems

Techniques are described herein for coding video content using recurrent-based machine learning tools. A device can include a neural network system including encoder and decoder portions. The encoder portion can generate output data for the current time step of operation of the neural network system based on an input video frame for a current time step of operation of the neural network system, reconstructed motion estimation data from a previous time step of operation, reconstructed residual data from the previous time step of operation, and recurrent state data from at least one recurrent layer of a decoder portion of the neural network system from the previous time step of operation. A decoder portion of the neural network system can generate, based on the output data and recurrent state data from the previous time step of operation, a reconstructed video frame for the current time step of operation.

Video compression using recurrent-based machine learning systems

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Amsterdam, Netherlands

Ties Jehan Van Rozendaal