Fast and efficient lossless image compression based on CUDA parallel wavelet tree encoding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Lossless image compression is in high demand in medical image applications. With the development of General Purpose Graphic Processing Unit (GPGPU) computing techniques, sequential lossless image compression algorithms can be modified to achieve more efficiency and speed. In this paper, a novel adaptation of Computation Unified Device Architecture (CUDA) to a wavelet tree based image compression algorithm is presented. Both transform phase and encoding phase of the compression algorithm have been redesigned for parallelization and efficiency. Our algorithm performs faster than the lossless JPEG-XR algorithm, with comparable compression ratios. Further improvements in speed and flexibility are also under current investigation.

Original languageEnglish
Title of host publication2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages21-24
Number of pages4
ISBN (Print)9781479940530
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014 - San Diego, CA, United States
Duration: Apr 6 2014Apr 8 2014

Publication series

NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

Conference

Conference2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014
CountryUnited States
CitySan Diego, CA
Period04/6/1404/8/14

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Keywords

  • CUDA
  • JPEG-XR
  • Lossless Image Compression
  • Wavelet Tree

Cite this

Ao, J., Mitra, S., & Nutter, B. (2014). Fast and efficient lossless image compression based on CUDA parallel wavelet tree encoding. In 2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014 - Proceedings (pp. 21-24). [6806019] (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSIAI.2014.6806019