Lossless image compression using causal block matching and 3D collaborative filtering

Robert Crandall, Ali Bilgin

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

7 Scopus citations

Abstract

Predictive coding has proven to be an effective method for lossless image compression. In predictive coding, untrans-mitted pixels are predicted based on the pixels already available at the decoder. Prediction errors are then compressed by entropy coding, and the original image can be reconstructed exactly at the decoder. More accurate prediction decreases the entropy of the prediction error, allowing for increased compression. Conventional image prediction methods rely on information from the immediate local neighborhood of each pixel. We introduce a novel predictor that leverages non-local structural similarities which have been shown to be effective in image denoising and deblurring applications. Experimental results show that the proposed method achieves state-of-the-art compression performance.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5636-5640
Number of pages5
ISBN (Print)9781479957514
DOIs
StatePublished - Jan 28 2014

Keywords

  • block matching
  • causal prediction
  • collaborative filtering
  • Lossless compression

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Crandall, R., & Bilgin, A. (2014). Lossless image compression using causal block matching and 3D collaborative filtering. In 2014 IEEE International Conference on Image Processing, ICIP 2014 (pp. 5636-5640). [7026140] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIP.2014.7026140