Uncompressed multimedia data requires considerable storage capacity and transmission bandwidth. Despite rapid progress in mass-storage density, processor speeds, and digital communication system performance, demand for data storage capacity and data-transmission bandwidth continues to outstrip the capabilities of available technologies. The recent growth of data intensive multimedia-based web applications even more sustained the need for more efficient ways to encode such data. There are two types of image compression schemes – lossless and lossy algorithms. In lossless compression schemes, the reconstructed image, after compression, is numerically identical to the original image. However lossless compression can only achieve a modest amount of compression. An image reconstructed following lossy compression contains degradation relative to the original. Often this is because the compression scheme completely discards redundant information. However, lossy schemes are capable of achieving much higher compression. The aim of this research is to create an efficient lossy image compression algorithm, using heuristic data clusterization methods; perform experiments of the new algorithm, measure its performance, analyze advantages and disadvantages of the proposed method, propose possible improvements and compare it with other popular algorithms. In this paper is presented new algorithm for image compression, which uses data base of popular image fragments. Proposed algorithm is... [to full text]

One of the images compression methods – fractal image compression is analyzed in the work. After work carried out, it is possible to state, that selecting parameters of method of fractal compression depends on user’s demands.