为了提高图象边缘提取时分类的准确性，在利用四叉树进行自适应图象分割的同时，将Laplacian算子作用于原始图象，然后从得到的边缘图像中，除去给定阈值的特定灰度层的图像后，即得到一种去除冗余，并突出边缘特征的图象，再将它应用于Domain块分类之中，可使分类结果更准确和更具客观性，计算机仿真解码实验结果表明，与其他同类的自动分形方法相比，该方法在编码速度、压缩比和恢复图象质量等方面均有显著提高。

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.