Statistical models for images: Compression, restoration and synthesis

Research output: Contribution to journalConference articlepeer-review


We present a parametric statistical model for visual images in the wavelet transform domain. We characterize the joint densities of coefficient magnitudes at adjacent spatial locations, adjacent orientations, and adjacent spatial scales. The model accounts for the statistics of a wide variety of visual images. As a demonstration of this, we've used the model to design a progressive image encoder with state-of-the-art rate-distortion performance. We also show promising examples of image restoration and texture synthesis.

Original languageEnglish (US)
Pages (from-to)673-678
Number of pages6
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
StatePublished - 1998
EventProceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA
Duration: Nov 2 1997Nov 5 1997

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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