A Note on the Computation of Optimal Minimum Mean-Square Error Quantizers

Asymptotically Optimal Scalable Coding for Minimum Weighted Mean Square Error

In this paper, we derive an asymptotically optimal multi-layer coding scheme for entropy-coded scalar quantizers (SQ) that minimizes the weighted mean-squared error (WMSE). The optimal entropy-coded SQ is non-uniform in the case of WMSE. The conventional multi-layer coder quantizes the base-layer reconstruction error at the enhancement-layer, and is sub-optimal for the WMSE criterion. We consid...

Minimum mean square error quantizers with uncorrelated input and quantization noise

Scalar quantizers with the minimum mean square error are designed in the case where the input signal and the quantization noise are uncorrelated. The probability density function (pdf) of the stochastic signal is assumed to be known. This design is then generalized to vector quantizers in the case where the expected value of the inner product between the input and quantization noise vector are ...

A Note on the Mean - Square Quantization Error

This report summarizes the understanding I have gained in my studies for the independent studies course ENEE 699 up to 15th April, 2000. It includes the derivation of an extension of a result of Wong and Brockett on the behaviour of scalar quantizers. 1 Control under limited communication Let, ẋ(t) = f(x(t), u(t)) + n(t) (1) be a controlled dynamical system where the state x(·) ∈ R, the control...

Note on a new computational data smoothing procedure suggested by minimum mean square error estimation

Notes on Linear Minimum Mean Square Error Estimators

Some connections between linear minimum mean square error estimators, maximum output SNR filters and the least square solutions are presented. The notes have been prepared to be distributed with EE 503 (METU, Electrical Engin.) lecture notes. 1 Linear Minimum Mean Square Error Estimators The following signal model is assumed: r = Hs + v (1) Here r is a N × 1 column vector denoting the observati...