FRACTAL IMAGE COMPRESSION ALGORITHMS BASED ON POSSIBILITY THEORY

Abstract

Two fractal image compression algorithms based on possibility theory are originally presented in this paper. Fuzzy sets are used to represent the edge character of each image block, and two kinds of membership function are designed. A fuzzy integrated judgement model is also proposed. The model generates an accurate value for each edge block, which would be a label during the search process. The edge possibility distribution function and the edge necessity level are designed to control the quantity of the blocks to be searched. Meanwhile the pre-restriction is proposed, the average intensity value at different locations is used to be a necessary condition before the MSE computations. It is shown by our experiments that the encoding times of our two algorithms, compared to that of Jacquin's approach, are reduced to 60%–70% and 10%–20%, respectively.