US 12,010,296 B2
Lossless image compression using block based prediction and optimized context adaptive entropy coding
Stefano Petrangeli, Mountain View, CA (US); Viswanathan Swaminathan, Saratoga, CA (US); and Haoliang Wang, San Jose, CA (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Aug. 18, 2022, as Appl. No. 17/891,057.
Application 17/891,057 is a continuation of application No. 17/177,592, filed on Feb. 17, 2021, granted, now 11,425,368.
Prior Publication US 2022/0400253 A1, Dec. 15, 2022
Int. Cl. H04N 19/105 (2014.01); H04N 19/176 (2014.01); H04N 19/182 (2014.01); H04N 19/91 (2014.01)
CPC H04N 19/105 (2014.11) [H04N 19/176 (2014.11); H04N 19/182 (2014.11); H04N 19/91 (2014.11)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
dividing an input image into a plurality of blocks, each block of the plurality of blocks being a portion of the input image;
determining a plurality of residual values using a pixel predictor for each block;
performing a classification of each feature of a set of features using a machine learning model feature selector trained to receive the input image and classify each feature of the set of features as belonging to a first subset of features or a second subset of features, wherein the set of features is defined by an image compression algorithm;
generating, by a machine learning context modeler, a residual cluster using the first subset of features and the plurality of residual values, wherein the generated residual cluster clusters one of more residual values of the plurality of residual values that have similar properties; and
entropy coding the residual clusters.