US 12,010,335 B2
Microdosing for low bitrate video compression
Abdelaziz Djelouah, Zürich (CH); Leonhard Markus Helminger, Zurich (CH); Roberto Gerson de Albuquerque Azevedo, Zurich (CH); Christopher Richard Schroers, Uster (CH); Scott Labrozzi, Cary, NC (US); and Yuanyi Xue, Kensington, CA (US)
Assigned to Disney Enterprises, Inc., Burbank, CA (US)
Filed by Disney Enterprises, Inc., Burbank, CA (US); and ETH Zürich (EIDGENÖSSISCHE TECHNISCHE HOCHSCHULE ZÜRICH), Zürich (CH)
Filed on Mar. 25, 2022, as Appl. No. 17/704,722.
Claims priority of provisional application 63/255,280, filed on Oct. 13, 2021.
Claims priority of provisional application 63/172,315, filed on Apr. 8, 2021.
Prior Publication US 2022/0337852 A1, Oct. 20, 2022
Int. Cl. H04N 19/42 (2014.01); H04N 19/124 (2014.01); H04N 19/13 (2014.01)
CPC H04N 19/42 (2014.11) 16 Claims
OG exemplary drawing
 
1. A system comprising:
a machine learning (ML) model-based video encoder; and
an ML model-based video decoder comprising a degradation-aware block based Micro-Residual-Network (MicroRN) defined by a number of hidden channels and a number of degradation-aware blocks of the MicroRN, the MicroRN configured to decode a first compressed video frame subset using a first decompression data, and decode a second compressed video frame subset using a second decompression data, without utilizing a residual network of a generative adversarial network (GAN) trained decoder;
the ML model-based video encoder configured to:
receive an uncompressed video sequence including a plurality of video frames;
determine, from among the plurality of video frames, a first video frame subset and a second video frame subset;
encode the first video frame subset to produce the first compressed video frame subset;
identify the first decompression data for the first compressed video frame subset;
encode the second video frame subset to produce the second compressed video frame subset; and
identify the second decompression data for the second compressed video frame subset.