Bounded-uncertainty estimation for correlated signal and noise

In this paper we present a class of bounded-uncertainty estimators as the solution of an estimation problem involving unknown statistics. The estimators are derived under the assumption of correlated signal and noise. The bounded-uncertainty framework gives an additional degree of freedom for estimator design that can benefit its performance. It also provides an indirect way of verifying hypotheses regarding unknown statistics for an application domain by examining the behavior of the estimator as a function of bound(s) placed on unknown statistics. If the unknown statistics are within a lower bound than the worst-case limit assumed by a minimax estimator, the quality of the estimation is increased. The derived estimators are applied to the filtering of quantization noise in coded and reconstructed video frames.