US 12,254,655 B2
Low-power fast-response machine learning variable image compression
Quang Le, San Jose, CA (US); Rajeev Nagabhirava, Santa Clara, CA (US); Kuok San Ho, Redwood City, CA (US); Daniel Bai, Freemont, CA (US); and Xiaoyong Liu, San Jose, CA (US)
Assigned to Western Digital Technologies, Inc., San Jose, CA (US)
Filed by Western Digital Technologies, Inc., San Jose, CA (US)
Filed on Jun. 29, 2021, as Appl. No. 17/362,427.
Prior Publication US 2022/0414942 A1, Dec. 29, 2022
Int. Cl. G06T 9/00 (2006.01); G01S 17/89 (2020.01); G06N 3/065 (2023.01); G06N 3/08 (2023.01); G06T 7/20 (2017.01); G11C 11/155 (2006.01)
CPC G06T 9/002 (2013.01) [G01S 17/89 (2013.01); G06N 3/065 (2023.01); G06N 3/08 (2013.01); G06T 7/20 (2013.01); G11C 11/155 (2013.01)] 26 Claims
OG exemplary drawing
 
1. A device, comprising:
an image sensor;
a Non-Volatile Memory (NVM); and
one or more processors communicatively coupled to the NVM, wherein the one or more processors are collectively configured to direct the device to:
receive image data from the image sensor for processing;
pass the received image data to a machine learning model;
recognize a plurality of subjects within the image data;
determine a region for each recognized subject;
classify the recognized subjects into one or more speed classifications;
generate a plurality of compression groups based on the speed classifications;
select a unique level of compression for each of the one or more compression groups;
compress the region of image data associated with each recognized subject according to the selected level of compression for the classified compression group;
compress at least a portion of the remaining image data utilizing a predetermined level of compression, the predetermined level being different from a selected unique level of compression associated with one of the one or more compression groups; and
store the variably compressed image data in the NVM.