Title :
Decomposed predictive transform estimation
Author_Institution :
Coll. of Staten Island, City Univ. of New York, NY, USA
fDate :
10/1/1994 12:00:00 AM
Abstract :
A novel design and implementation decompositions are found to arise for a minimum mean squared error (MMSE) linear predictive transform (LPT) estimator when certain symmetry conditions are satisfied by the first- and second-order statistics used to design the estimator. This results in a decomposed LPT estimator whose design and implementation computational effort is significantly less than that of the original estimator
Keywords :
digital circuits; encoding; image processing; least squares approximations; linear predictive coding; matrix algebra; parameter estimation; signal processing; statistics; transforms; computational effort; decomposed predictive transform estimation; design; first-order statistics; implementation decompositions; linear predictive transform; minimum mean squared error; second-order statistics; symmetry conditions; Communication system control; Decoding; Equations; Kalman filters; Multidimensional systems; Process control; Signal design; Signal processing; Source coding; State-space methods;
Journal_Title :
Signal Processing, IEEE Transactions on
