US 12,481,877 B2
Instance-adaptive image and video compression in a network parameter subspace using machine learning systems
Johann Hinrich Brehmer, Amsterdam (NL); Ties Jehan Van Rozendaal, Amsterdam (NL); Yunfan Zhang, Amsterdam (NL); and Taco Sebastiaan Cohen, Amsterdam (NL)
Assigned to QUALCOMM Incorporated, San Diego, CA (US)
Filed by QUALCOMM Incorporated, San Diego, CA (US)
Filed on Aug. 25, 2021, as Appl. No. 17/411,936.
Prior Publication US 2023/0074979 A1, Mar. 9, 2023
Int. Cl. G06N 3/08 (2023.01); G06F 18/211 (2023.01)
CPC G06N 3/08 (2013.01) [G06F 18/211 (2023.01)] 30 Claims
OG exemplary drawing
 
1. A method of processing image data, comprising:
receiving input data for compression by a neural network compression system;
determining, based on the input data, a set of updated model parameters for the neural network compression system, wherein the set of updated model parameters is selected from a subspace of model parameters based on minimizing, at inference time of a rate-distortion autoencoder (RD-AE) of the neural network compression system, a combined rate-distortion-model rate (RDM) loss corresponding to a rate-distortion loss of the RD-AE at the inference time and a number of model update bits used to represent the subspace of model parameters:
generating at least one bitstream including a compressed version of the input data and a compressed version of one or more subspace coordinates that correspond to the set of updated model parameters; and
outputting the at least one bitstream for transmission to a receiver.