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Image representation and compression using directionlets

  • Vladan Velisavljevic
    ,
  • Baltasar Beferull-Lozano
    ,
  • Martin Vetterli
    ,
  • Pier Luigi Dragotti
    ,
  • Dimitri Van De Ville
    ,
  • Vivek K. Goyal
  • University of Valencia
    ,
  • Swiss Federal Institute of Technology Lausanne
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 or contours. These features, being elongated and characterized by geometrical regularity along different directions, intersect and generate many large magnitude wavelet coefficients. Since contours are very important elements in visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. We propose a construction of critically sampled perfect reconstruction transforms with directional vanishing moments (DVMs) imposed in the corresponding basis functions along different directions, called directionlets. We also demonstrate the outperforming non-linear approximation (NLA) results achieved by our transforms and we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method beats the standard SFQ both in terms of mean-square-error (MSE) and visual quality, especially in the low-rate compression regime. We also show that our compression method, does not increase the order of computational complexity as compared to the standard SFQ algorithm.

Publication Information

Output type

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

Original language

English

Publication milestones

  • Published - 20/09/2007

Publication status

Published - 20/09/2007

Publisher

SPIE, United States

ISBN (Print)

9780819468499

Publication IDs

  • handle.net: 10547/292640
  • Scopus: 42149149212

Host publication title

nan

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