An Attribute Computing Network-based Fractal Image Compression Method

Fractal image compression coding is a high compression ratio of new image coding method. The approach makes use of the image of the self-similarity, the image is compressed. Fractal image compression coding is based on the mathematical Iterated Function System (IFS), Collage Theorem. With the IFS for the theoretical foundation of the Jacquin [40] automatic fractal image coding method high compression ratio and the rapid decoding resolution had nothing to do with many advantages, such as But its very time-consuming encoding process greatly limits the fractal coding practical process. In this paper, A. E. Jacquin [40] the basic block-based fractal image compression methods, Through the Image Coding Theory and the right in recent years fractal image compression coding algorithm, the study found that Attribute computing network mapping the qualitative model and the conversion function with the affine transform is a shocking similarity (in Chapter V show). Therefore proposed and implemented a network based on the calculated properties of fractal image compression method. Such attributes computing network based-on the fractal image compression coding combination of a sub-block division, block the merger, qualitative mapping, Construction conversion function and fractal image coding method, the fractal compression space mapping theorem, we can see For a given graph IFS code, using random iteration, the graphics can draw attractor, That is to say if the IFS code to modeling, using a very small amount of code can map out a very complex graphics effects. This process of inverse process is the image compression process, starting from a graphics IFS access code, is equivalent to the original graphics were highly compressed. This paper is based on such thinking compression. Attribute-Based Computing Network fractal image compression with compression ratio and image quality features. In the second chapter describes the fractal, the fractal dimension of the basic concepts of fractal theory of the basic tenets of image compression, Fractal Image Compression and the fundamental principles Criterion, contraction affine transformation, Iterated Function System, as well as benchmarking of space mapping and Banach fixed-point theorem. In this paper, the third chapter is devoted to the research advances, the major focus is on improving coding speed, compression ratio and coding results, improve decoding speed, and other methods of fractal coding. In this paper, the fourth chapter describes the fractal compression algorithm and improved algorithm, including Jacquin basic algorithm and Quadtree law. In this paper, the fifth chapter focuses on the calculation based on the attributes of the network fractal compression algorithm, focus on the attributes of computing network qualitative mapping with fractal graphics, or compressing the theoretical foundation, through experiments and get a good result.