US 12,347,149 B2
System, method, and computer program for content adaptive online training for multiple blocks in neural image compression
Ding Ding, Palo Alto, CA (US); Wei Wang, Palo Alto, CA (US); and Shan Liu, Palo Alto, CA (US)
Assigned to TENCENT AMERICA LLC, Palo Alto, CA (US)
Filed by TENCENT AMERICA LLC, Palo Alto, CA (US)
Filed on Sep. 22, 2022, as Appl. No. 17/950,569.
Claims priority of provisional application 63/289,033, filed on Dec. 13, 2021.
Prior Publication US 2023/0186525 A1, Jun. 15, 2023
Int. Cl. G06T 9/00 (2006.01); G06N 3/045 (2023.01); G06T 3/4046 (2024.01)
CPC G06T 9/002 (2013.01) [G06N 3/045 (2023.01); G06T 3/4046 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method of content-adaptive online training for end-to-end (E2E) neural image compression (NIC) using a neural network performed by at least one processor, the method comprising:
receiving, by an E2E NIC framework, an input image including one or more blocks;
preprocessing a first neural network of the E2E NIC framework, based on the one or more blocks;
computing updated parameters using the preprocessed first neural network, wherein the updated parameters include a learning rate and a number of steps, and wherein the learning rate and the number of steps are selected based on characteristics of the input image;
encoding the one or more blocks and the updated parameters;
updating the first neural network based on the encoded updated parameters; and
generating a compressed representation of the encoded one or more blocks using the updated first neural network.