Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients

Juan Liu, Pierre Moulin

Research output: Contribution to journalArticlepeer-review


This paper presents an information-theoretic analysis of statistical dependencies between image wavelet coefficients. The dependencies are measured using mutual information, which has a fundamental relationship to data compression, estimation, and classification performance. Mutual informations are computed analytically for several statistical image models, and depend strongly on the choice of wavelet filters. In the absence of an explicit statistical model, a method is studied for reliably estimating mutual informations from image data. The validity of the model-based and data-driven approaches is assessed on representative real-world photographic images. Our results are consistent with recent empirical observations that coding schemes exploiting inter- and intrascale dependencies alone perform very well, whereas taking both into account does not significantly improve coding performance. A similar observation applies to other image processing applications.

Original languageEnglish (US)
Pages (from-to)1647-1658
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number11
StatePublished - Nov 2001


  • Image compression
  • Image modeling
  • Image restoration
  • Markov processes
  • Mutual information
  • Rate-distortion
  • Wavelets

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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