Fractal coding of digital image offers many promising qualities. However the coding process suffers from the long search time of the matching between range blocks and domain blocks. So

classifying the blocks is usually used to reduce the long search time. The classifying is based on the special characters of the image blocks. And the special characters are presented as smooth area

edge area and texture area in an image. For an image block

smooth area can be considered as non-edge area

texture area also can be considered as quickly changed edge area. So important information of image character can be obtained through edge extraction. Edge extraction usually takes the use of several edge-detecting operators such as Sobel and Laplacian operators. Here

a Laplacian operator is applied in original image first because the operator can detect edge image with quickly changed. Then

from the edge image after above operation

a given-threshold gray level image is removed. So a redunance-removed and an edge character prominent image is obtained. This image is used for domain blocks classification. And several parameters are obtained by statistics or multiplied from the image. Finally 4 big classes and 12 small classes are derived from above parameters and all classes are used for fractal image coding.The proposed fractal method is tested by real gray level images under windows 98 platform with visual C language. For encoding an image effectively

in this paper

a quad-tree structure is used for image segmentation so that the encoding algorithm is an automatic iterative algorithm.Computer encoding and decoding simulation result shows that compared with other similar methods

the proposed fractal method can make more accurate and objective classifications

and has an improved encoding speed

compression ratio and can provide a better reconstructed image quality.