Estimation of local statistics for digital processing of nonstationary images

In this correspondence we report results from our experiments to find useful measures of local image autocovariance parameters from small subblocks of data. Our criterion for the reliability of parameter estimates is that they should correlate with observed signal activity and yield high quality results when used in adaptive processing. We describe a method for estimating the correlation parameters of first-order Markov (nonseparable exponential) autocovariance models, The method assumes that image data are stationary within N × N pixel sub-blocks. Values of the autocovariance parameters may be calculated at every pixel location. A value of N = 16 yields results which fit our criterion, even when the original data are degraded by blur and noise. An application to data compression is suggested.