The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 17, 2024

Filed:

Aug. 04, 2023
Applicant:

Deep Render Ltd, London, GB;

Inventors:

Chri Besenbruch, London, GB;

Ciro Cursio, London, GB;

Christopher Finlay, London, GB;

Vira Koshkina, London, GB;

Alexander Lytchier, London, GB;

Jan Xu, London, GB;

Arsalan Zafar, London, GB;

Assignee:

DEEP RENDER LTD., London, GB;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 19/126 (2014.01); G06N 3/045 (2023.01); G06N 3/084 (2023.01); G06T 3/4046 (2024.01); G06T 9/00 (2006.01); G06V 10/774 (2022.01); H04N 19/13 (2014.01);
U.S. Cl.
CPC ...
H04N 19/126 (2014.11); G06N 3/045 (2023.01); G06N 3/084 (2013.01); G06T 3/4046 (2013.01); G06T 9/002 (2013.01); G06V 10/774 (2022.01); H04N 19/13 (2014.11);
Abstract

There is disclosed a computer-implemented method for lossy image or video compression, transmission and decoding, the method including the steps of: (i) receiving an input image at a first computer system; (ii) encoding the input image using a first trained neural network, using the first computer system, to produce a latent representation; (iii) quantizing the latent representation using the first computer system to produce a quantized latent; (iv) entropy encoding the quantized latent into a bitstream, using the first computer system; (v) transmitting the bitstream to a second computer system; (vi) the second computer system entropy decoding the bitstream to produce the quantized latent; (vii) the second computer system using a second trained neural network to produce an output image from the quantized latent, wherein the output image is an approximation of the input image. Related computer-implemented methods, systems, computer-implemented training methods and computer program products are disclosed.


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