Poisson-noise removal in Self-similarity studies based on Packet-counting: Factorial-Moment/Strip-Integral approach

摘要

In this work we point out that some common methods for estimating self-similarity parameters - involving packet counting for the estimate of statistical moments - are affected by distortion at the finest resolutions and quantization errors and we illustrate - using also a small sample of the Bellcore data set - a technique for removing this undesirable effect, based on factorial moments and strip integrals. Then we extend the strip-integral approach to the approximation of the square of the Haar wavelet coefficients, for the estimate of the Hurst self-affinity exponent.