Lossless Image Compression Using Causal Block Matching and 3D Collaborative Filtering

Technology #ua14-106

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Categories
Researchers
Robert Crandall
Graduate Associate, Program in Applied Mathematics
Ali Bilgin
Assistant Professor, Biomedical Engineering
Managed By
John Geikler
Asst. Director, Physical Science Licensing (520) 626-4605

Title: Lossless Image Compression Using Causal Block Matching and 3D Collaborative Filtering

Invention: This technology is a novel predictor that leverages non-local structure similarities to de-noise and de-blur images by extending block-matching and 3D collaborative filtering to image prediction and compression.

Background:  Over the last few decades, substantial progress has been made in estimation methods that are adapted to the local structure in multidimensional image and video data. Block-matching and 3D collaborative filtering are algorithms developed in the last decade to aid in image de-noising, rendering older methods obsolete.  

 

Applications:

  • Incorporation into a codec could provide excellent compression performance for image and video compression.
  • Satellite imaging
  • Medical imaging

Advantages:

  • Time-dependent; addresses the issue of time-delay and increased resource use
  • De-noises and de-blurs images  

 

Contact:

John Geikler

Assistant Director, Physical Sciences

Phone: 520.626.4605

JohnG@tla.ariozna.edu