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

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 information is computed analytically for several statistical image models, and depends strongly on the choice of wavelet filters. In the absence of an explicit statistical model, a method is studied for reliably estimating mutual information 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 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.