US 12,470,715 B2
Neural-network media compression using quantized entropy coding distribution parameters
Amir Said, San Diego, CA (US)
Assigned to QUALCOMM Incorporated, San Diego, CA (US)
Filed by QUALCOMM Incorporated, San Diego, CA (US)
Filed on Dec. 8, 2023, as Appl. No. 18/534,073.
Application 18/534,073 is a continuation of application No. 17/814,426, filed on Jul. 22, 2022, granted, now 11,876,969.
Claims priority of provisional application 63/267,857, filed on Feb. 11, 2022.
Prior Publication US 2024/0121392 A1, Apr. 11, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. H01L 29/94 (2006.01); H04N 19/124 (2014.01); H04N 19/13 (2014.01); H04N 19/134 (2014.01); H04N 19/136 (2014.01); H04N 19/42 (2014.01)
CPC H04N 19/13 (2014.11) [H04N 19/124 (2014.11); H04N 19/134 (2014.11); H04N 19/136 (2014.11); H04N 19/42 (2014.11)] 18 Claims
OG exemplary drawing
 
1. A method of coding media data, the method comprising:
determining a probability distribution function parameter for a data element of a data stream coded by a neural-based media compression technique, wherein the probability distribution function parameter is a function of a standard deviation of a probability distribution function of the data stream and wherein the probability distribution function parameter is based on a distribution of the data stream optimized for quantization,
determining a code vector based on the probability distribution function parameter; and
entropy coding the data element using the code vector.