Spectral estimation for multiple 2-D signals by model-based methods is developed. The procedures compute the entire spectral matrix of autospectra and cross spectra for the set of 2-D signals. Spectral analysis by autoregressive AR modeling is studied extensively. Specific differences between AR models for this problem and those for lower dimensional problems are highlighted. An extension of the Jackson-Chien method for combining estimates with single quadrant support is proposed and a method is developed for estimating the model parameters directly from the data i.e. without prior estimation of a correlation matrix. A measure of the similarity of two spectral estimates based on the statistical divergence is proposed and used to compare various spectral estimates. Keywords Signal modeling, Linear prediction, Image coding, Maximum likelihood method, Spectral estimation.
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