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Low-rate reduced complexity image compression using directionlets

  • University of Valencia
    ,
  • University of California at Berkeley
    ,
  • Swiss Federal Institute of Technology Lausanne
    ,
  • Imperial College London
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

The standard separable two-dimensional (2-D) wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to capture efficiently one-dimensional (1-D) discontinuities, like edges and contours, that are anisotropic and characterized by geometrical regularity along different directions. In our previous work, we proposed a construction of critically sampled perfect reconstruction anisotropic transform with directional vanishing moments (DVM) imposed in the corresponding basis functions, called directionlets. Here, we show that the computational complexity of our transform is comparable to the complexity of the standard 2-D WT and substantially lower than the complexity of other similar approaches. We also present a zerotree-based image compression algorithm using directionlets that strongly outperforms the corresponding method based on the standard wavelets at low bit rates

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 20/02/2007

Publication status

Published - 20/02/2007

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

1424404819

ISBN (Electronic)

1424404819

External Publication IDs

  • handle.net: 10547/293059
  • Scopus: 67649591081

Host publication title

nan

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