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ULTRA-HIGH COMPRESSION OF IMAGES BASED ON DEEP LEARNING

  • US 20160292589A1
  • Filed: 04/03/2015
  • Published: 10/06/2016
  • Est. Priority Date: 04/03/2015
  • Status: Active Grant
First Claim
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1. A computer implemented method for machine learning model parameters for image compression, comprising:

  • partitioning a plurality of image files stored on a first computer memory into a first set of regions;

    determining a first set of machine learned model parameters based on the first set of regions, the first set of machine learned model parameters representing a first level of patterns in the plurality of image files;

    constructing a representation of each region in the first set of regions based on the first set of machine learned model parameters;

    constructing representations of the plurality of image files by combining the representations of the regions in the first set of regions;

    partitioning the representations of the plurality of image files into a second set of regions;

    determining a second set of machine learned model parameters based on the second set of regions, the second set of machine learned model parameters representing a second level of patterns in the plurality of image files; and

    storing the first set of machine learned model parameters and the second set of machine learned model parameters on one or more computer memories.

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