Research On Non-stationary Data Compression Algorithm

Finding an effective analytical method on non-stationary signal is one of the hot issues in both academia and engineering circle and it is of extremely important significance to signal analysis for data compression. Traditional signal processing methods describe the signals in the time or frequency domain separately. It provides neither precise time or frequency positioning nor information of frequency at any local time segment. Since parameters of non-stationary signals vary by time, the traditional approaches are less effective.This thesis gives an intensive study on non-stationary signal compression algorithm and its implementation based on the theories of numerical approaching. The main work is summarized as follows:1. Based on curve fitting and classification a non-stationary decomposition algorithm is presented. Studies on non-stationary signals with slow monotonic change have been performed. With the improved empirical mode decomposition algorithm, it is able to pre-process signals and to eliminate noise in the signals. The main rule of the change is described by the curve fitting. The classification algorithm is used to decompose the spectrum images into two categories as main or non-main spectrum domain, since the spectrum images have large volumes of data and the data correlation is poor. Different methods will be applied to each data domain and effective description of observation data will be obtained.2. The empirical data decomposition algorithm is presented. The idea of the empirical data decomposition was extracted by polynomial description of the non-stationary data. The mean of the local area data is used as a benchmark and the main change rule of data is described by the function. The sum of the function and the error denotes the local area data. The general structure of the empirical data decomposition is founded, and the design rules of the analysis filter are presented.3. The detailed implementation process of the empirical data decomposition is provided. The parameters of the predictive filter in the algorithm are designed by use of adaptive optimization and cubic spline interpolation functions, respectively. Different decomposition structures are presented for continuous-tone image data and non-continuous tone image data. The characteristics of empirical data decomposition algorithm are analyzed by simulation.4. A parallel algorithm for data decomposition module and bit plane encoding and decoding module is proposed to improve efficiency of non-stationary signal encoding and decoding system. The parallel algorithm framework for the two modules is presented. Relations between the time of encoding and decoding and the number of kernel are analyzed by simulation.