Abstract
In this paper, the symmetric normal inverse gaussian (SNIG) probability density function (PDF) is proposed as a highly suitable prior for modelling the DCT coefficients of natural images. A new method, based on minimizing the Kullback-Leibler divergence between the proposed prior and the empirical PDF extracted from image data, is proposed to estimate the SNIG parameters. The efficacy of the proposed parameter estimation technique is tested using Monte-Carlo simulations. It is shown that the SNIG PDF is a more effective prior as compared to the generalized Gaussian (GG), α-stable, and Laplacian PDFs for modelling the full-frame DCT coefficients of natural images. For the block-DCT coefficients, the SNIG PDF is shown to be better than the GG and Laplacian PDFs, and comparable to the α-stable one, while incurring much less complexity for parameter estimation.
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