The aim of this study is to improve the accuracy of flicker parameters estimation in old film sequences in which moving objects are present. Conventional methods tend to fail in flicker parameters estimation due to the effects of moving objects. Our proposed method firstly utilizes an adaptive Gaussian mixture model (GMM)-based method to detect the moving objects in the film sequences, and combines the detected results with the histogram-matched frames to generate reference frames for flicker parameters estimation. Then, on the basis of a linear flicker model, the proposed method uses an M-estimator with the reference frames to estimate the flicker parameters. Experimental results show that the proposed method can effectively improve the accuracy of flicker parameters estimation when the moving objects are present in the film sequences.

In this paper, we propose a robust adaptive algorithm for impulsive noise suppression. The perturbation of the input signal as well as the perturbation of the estimation error are restricted by M-estimation. The threshold used in M-estimation is obtained from the proposed adaptive variance estimation. Simulations show that the proposed algorithm is less vulnerable to the impulsive noise than the conventional algorithm.

Network dimensioning is an important issue to provide stable and QoS-rich communication services. A reliable estimation of bandwidths of links between the end-to-end path is a first step towards the network dimensioning. Pathchar is one of such tools for the bandwidth estimation for every link between two end hosts. However, pathchar still has several problems. If unexpectedly large errors are included or if route alternation is present during the measurement, the obtained estimation is much far from the correct one. We investigate the method to eliminate those errors in estimating the bandwidth. To increase the reliability on the estimation, the confidence interval for the estimated bandwidth is important. For this purpose, two approaches, parametric and nonparametric approaches, are investigated to add the confidence intervals. Another important issue is the method for controlling the measurement period to eliminate the measurement overheads. In this paper, we propose a measurement method to adaptively control the number of measurement data sets. Through experimental results, we show that our statistical approaches can provide the robust estimation regardless of the network conditions.