Classification and feature vector techniques to improve fractal image coding

Fractal image compression receives much attention because of its desirable properties like resolution independence, fast decoding and very competitive rate-distortion curves. Despite the advances made in fractal image compression the long computing time in encoding phase still remain as main drawback of this technique as encoding step in computationally expensive. A large number of sequential searches through portions of the image are carried out to identify best matches for other image portions. So far, serveral methods have been proposed in order to speed-up fractal image coding. Here an attempt is made to analyze the speed-up techniques like classification and feature vector, which demonstrates the search through larger portions of the domain pool without increasing computation time. In this way both the image quality and compression ratio are improved at reduced computation time. Experimental results and analysis show that proposed method can speed up fractal image encoding process over conventional methods.