Fractal theory has strengths such as high compression rate and fast decoding time in application to image compression, but it suffers from long comparison time necessary for finding an optimally similar domain block in the encoding stage. This paper proposes a neural network based block classifier which enhances the encoding time significantly by classifying domain blocks into 4 patterns and searching only those blocks having the same pattern with the range block to be encoded. Size of a block is differently determined depending on the image complexity of the block. The proposed algorithm has been tested with three different images having various featrues. The experimental results have shown that the proposed algorithm enhances the compression time by 40% on average compared to the conventional fractal encoding algorithms, while maintaining allowable image qualify of PSNR 30 dB.