Deep Render Ltd.

Chris Finlay

Arsalan Zafar

Christian Etmann

Bilal Abbasi

Sebastjan Cizel

Chri Besenbruch

Jan Xu

Vira Koshkina

Jonathan Rayner

Aleksandar Cherganski

Christian Besenbruch

A method for lossy video encoding, transmission and decoding, the method comprising the steps of: receiving a first frame and a second frame at a first computer system; determining a first flow between the first frame and the second frame; determining a second flow based on the first frame and the second frame; encoding an input based on the first flow and the second flow using a first trained neural network to produce a latent representation; transmitting the latent representation to a second computer system; decoding the latent representation using a second trained neural network to produce an output flow; and using the output flow to obtain an output frame, wherein the output frame is an approximation of the second frame.

Method and data processing system for lossy image or video encoding, transmission and decoding

A method of training one or more neural networks, the one or more neural networks being for use in lossy image or video encoding, transmission and decoding, the method comprising the steps of: receiving an input image at a first computer system; encoding the input image using a first neural network to produce a latent representation; decoding the latent representation using a second neural network to produce an output image, wherein the output image is an approximation of the input image; evaluating a function based on a difference between the output image and the input image; updating the parameters of the first neural network and the second neural network based on the evaluated function; and repeating the above steps using a first set of input images to produce a first trained neural network and a second trained neural network; wherein the difference between the output image and the input image is determined based on the output of a neural network acting as a discriminator; the parameters of the neural network acting as a discriminator are additionally updated based on the evaluated function; and the parameters of the neural network acting as a discriminator are updated at a first learning rate; wherein, after at least one of the updates of the parameters of the neural network acting as a discriminator, the first learning rate is updated; and the update to the first learning rate is based on an error of the output of the neural network acting as a discriminator.

A method for lossy video encoding, transmission and decoding, the method comprising the steps of: receiving an input video at a first computer system; encoding an input frame of the input video to produce a latent representation; producing a quantized latent; producing a hyper-latent representation; producing a quantized hyper-latent; entropy encoding the quantized latent; transmitting the entropy encoded quantized latent and the quantized hyper-latent to a second computer system; decoding the quantized hyper-latent to produce a set of context variables, wherein the set of context variables comprise a temporal context variable; entropy decoding the entropy encoded quantized latent using the set of context variables to obtain an output quantized latent; and decoding the output quantized latent to produce an output frame, wherein the output frame is an approximation of the input frame.

A method for lossy image or video encoding, transmission and decoding, the method comprising the steps of: receiving an input image at a first computer system; encoding the first input training image using a first trained neural network to produce a latent representation; performing a quantization process on the latent representation to produce a quantized latent; entropy encoding the quantized latent using a probability distribution, wherein the probability distribution is defined using a tensor network; transmitting the entropy encoded quantized latent to a second computer system; entropy decoding the entropy encoded quantized latent using the probability distribution to retrieve the quantized latent; and decoding the quantized latent using a second trained neural network to produce an output image, wherein the output image is an approximation of the input training image.

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London, United Kingdom

Chris Finlay