SSP'05 -- Proceedings

	IEEE/SP 13th workshop on Statistical Signal Processing July, 17-20, 2005 - Bordeaux - France

Information regarding the paper

Title

Synthesis Models For N-Dimensional Discrete-Space Self-Similar Signals

Author(s)

Rajesh Narasimha	Georgia Tech, USA
Seungsin Lee	Samsung Research, Korea
Raghuveer Rao	Rochester Institute of Technology, USA

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Abstract

New formulations and models are proposed for describing statistical self-similarity in general N-dimensional settings. By using a matrix scaling operator for defining statistical self-similarity, a wide class of continuous-space N-D processes can be characterized as self-similar with respect to specific matrix classes. Further, discrete-space versions of N-D statistical self-similarity are treated through a discrete-domain scaling operation. The mathematical basis for the approaches is provided along with 2-D synthesis examples.