Research On Novel Image Compression Algorithms Based On Cellular Automata

Image compression becomes increasingly important to support efficient storage, transmission, but the traditional compression algorithms can no longer meet the need of achieving much higher compression. Researchers in the field of image compression have focused their attention on the study of some new kinds of compression model such as artificial life. Theory and applications of the Cellular Automata (CA) as one of the directions of artificial intelligence has become an important research field of data compression in recent years.CA is dynamical systems exhibiting many notable features, namely, nonlinear, massive parallelism, and discreteness locality of cellular interactions, etc. In this paper,we study Cellular Automata (CA) as a modeling tool successfully using in image compression. The dissertation is organized as follows:First of all, the theoretic and analysis of some CA modeling are reported in Chapter2. We describe the advantages and disadvantages of the efficient compression algorithm basing on CA modeling compared with the traditional methods. Their potential within image compression is being investigated.In Chapter3 reports a new Cellular Automata (CA) model for binary image compression. The search for appropriate CA having the desired characteristics to system is an extremely difficult task in view of exponentially large search space, The genetic programming (GP) has been employed to search for optimal non-linear cellular automata rules which performs the binary image compression task. The simulation research proves that the algorithm is feasible and more efficient in compression ratio, compression speed, decompression precision and the code can be compressed using other compression algorithms etc.Compression of grayscale image data using CA model is also proposed. We presents a new quick code scheme of vector quantization based on CA. The effective- ness of code was evaluated by comparing the other methods.Chapter4 reports the characterization of fuzzy cellular automata. The analytical study the possible compression using Fuzzy cellular automata has been done and the Algorithm is also proposed and analyzed.Chapter5 describes the image compression method based on Cellular Neural Networks (CNN). The parallel CNN model is used for DCT image data compression. Finally Chapter 6 concludes the volume, presenting several possible avenues of future research.