Accelerating Fractal Image Encoding Based on Gray Value Moment Features of Normalized Block

Abstract

Fractal image encoding with full search typically requires a very long runtime, which is essentially spent on searching for the best-matched block to an input range block in a large domain pool. This paper thus proposed an effective method to improve the drawback, which is mainly based on gray value moment features of normalized block and related inequality is presented by the authors. During the search process, the gray value moment features is first utilized to confine efficiently the search space to the vicinity of the initial-matched block (i.e., the domain block having the closest gray value moment features to the input range block being encoded), aiming at reducing the searching scope of similarity matching to accelerate the encoding process. Simulation results show that the proposed scheme not only reduce the searching scope of best-matched to accelerate the encoding process, but also can obtain good quality of the reconstructed images as compared to the baseline algorithm with full search.