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Space-frequency quantization for image compression with directionlets

  • Deutsche Telekom
    ,
  • University of Valencia
    ,
  • University of California at Berkeley
    ,
  • Swiss Federal Institute of Technology Lausanne
Research Output: Contribution to journal Article Peer-review

Abstract

The standard separable 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 efficiently capture 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 the visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. In our previous work, we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments imposed in the corresponding basis functions along different directions, called directionlets. In this paper, we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method outperforms the standard SFQ in a rate-distortion sense, both in terms of mean-square error 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: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 1761-1773 (13 pages)

Journal (Volume, Issue Number)

IEEE Transactions on Image Processing (Volume 16, Issue 7)

Publication milestones

  • Published - 01/01/2007

Publication status

Published - 01/01/2007

ISSN

1057-7149

External Publication IDs

  • handle.net: 10547/292583
  • Scopus: 34347389222
  • PubMed: 17605375

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