Fractal Image Compression on Hexagonal Structure

Image compression has many applications. For example, it is an important step for distributed and network based pattern recognition. For real time object recognition or reconstruction, image compression can greatly reduce the image size, and hence increase the processing speed and enhance performance. Fractal image compression is a relatively recent image compression method. Its basic idea is to represent images as a fixed point of a contractive Iterated Function System (IFS). Spiral Architecture (SA) is a novel hexagonal image structure on which images are displayed as a collection of hexagonal pixels. The efficiency and accuracy of image processing on SA have been demonstrated in many recently published papers. We have shown the existence of contractive IFS’s through the construction of a Complete Metric Space on SA. The construction of range and domain blocks for fractal image compression on SA is highly related to the uniform image separation specific to SA. In this paper, two presentations of SA on the traditional display device will be discussed. Then we will review the current research work on fractal image compression based on SA using both presentations. The performance on SA will be compared with it on the traditional square structure in terms of compression ratio and PSNR. In the experimental results, higher PSNR values can be achieved at various compression ratios for all test images. Keywords--fractal coding, hexagonal image structure, image storage, image addressing, Spiral Architecture.