It is important to accurately fit the unknown probability density functions of background or object. To solve this problem,
the Burr distribution is introduced. Three-parameter Burr distribution can cover a wide range of distribution. The
expectation maximization algorithm is used to deal with the estimation difficulty in the Burr distribution model. The
expectation maximization algorithm starts from a set of selected appropriate parameters’ initial values, and then iterates
the expectation-step and maximization-step until convergence to produce result parameters. The experiment results show
that the Burr distribution could depicts quite successfully the probability density function of a significant class of image,
and comparatively the method has low computing complexity.
The Guassian distribution model is often used to characterize the statistical behavior of image or other multimedia signal,
and applied in fitting probability density functions of a signal. But, in practically, the probability density function of data
source may be inherently non-Gaussian. As the distribution family covers most of the common distribution types and the
frequency curves provided by the family are as wide as in general use, this paper considers Johnson distribution family to
estimate the unknown parameters and approximate the empirical distribution. The method uses the moments to initialize
the parameters of the distribution family, and then calculates parameters by using EM algorithm. The experiment results
show that the fitted model could depicts quite successfully the both Gaussian and non-Gaussian probability density
function of image intensity, and comparatively the method has low computing complexity.
Time complexity is one of the biggest problems for fractal image compression algorithm which can bring about high
compression ratio. However, there is inherently data parallelism for fractal image compression algorithm. Naturally,
parallel computation scheme would be used to deal with it. This paper uses "equal division load" balancing algorithm to
design parallel fractal coding algorithm and implement the fractal image compression. "Equal division load" balancing
algorithm distributes computation tasks to all processors equally. Load in every node is divided into smaller tasks based
on all power of nodes on network, and then these smaller tasks are sent to corresponding nodes to balance the load
among nodes. Analysis shows that the algorithm greatly reduces the component task execution time.