Optimal estimators are derived for a signal-dependent film-grain noise model, and the effect of signal-dependence on the estimators's structures is investigated. Due to the mathematical complexity of these optimal estimators, various suboptimal estimators are proposed. Computer simulations are then presented which compare the optimal and suboptimal estimators with regard to mean square estimation error, sensitivity to signal-dependence, and robustness with respect to the a priori signal probability density function.
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Image compression in signal-dependent noise.
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Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, Texas 78711-1084, USA.
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