US 12,335,486 B2
Content-aware, machine-learning-based rate control
Eshed Ram, Nofit (IL); Dotan David Levi, Kiryat Motzkin (IL); Assaf Hallak, Tel Aviv (IL); Shie Mannor, Haifa (IL); Gal Chechik, Ramat Hasharon (IL); Eyal Frishman, Hod Hasharon (IL); Ohad Markus, Haifa (IL); Dror Porat, Haifa (IL); and Assaf Weissman, Moreshet (IL)
Assigned to Mellanox Technologies, Ltd., Yokneam (IL)
Filed by Mellanox Technologies, Ltd., Yokneam (IL)
Filed on Jan. 12, 2023, as Appl. No. 18/096,428.
Prior Publication US 2024/0244225 A1, Jul. 18, 2024
Int. Cl. H04N 19/147 (2014.01); H04N 19/124 (2014.01); H04N 19/172 (2014.01)
CPC H04N 19/147 (2014.11) [H04N 19/124 (2014.11); H04N 19/172 (2014.11)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
a processing device to:
receive video content, metadata related to the video content, and a target bit rate for encoding the video content; and
detect a content type of the video content based on one or more tags within the metadata, wherein the one or more tags are indicative of the content type received from a particular video streaming source device;
encoding hardware to perform frame encoding on the video content and to generate frame statistics based on one or more encoded frames of the video content corresponding to a current frame; and
a controller coupled between the processing device and the encoding hardware, the controller programmed with machine instructions to:
receive the frame statistics from the encoding hardware;
generate a first quantization parameter (QP) value of the current frame using a frame machine learning model with a first plurality of weights, wherein the first plurality of weights depends at least in part on the content type, the target bit rate, and the frame statistics; and
provide the first QP value directly to the encoding hardware for rate control of the frame encoding.