Traditionally, fractal image compression suffers from lengthy encoding time in measure ofhours. In this paper, combined with characteristlcs of human visual system, a flexible classification technique is proposed. This yields a corresponding adaptive algorithm which can cut down the encoding timeinto second’s magnitude. Experiment results suggest that the algorithm can balance the overall encodingperformance efficiently, that is, with a higher speed and a better PSNR gain. Traditionally, fractal image compression suffers from lengthy encoding time in measure of hours. In this paper, combined with characteristlcs of human visual system, a flexible classification technique is proposed. This yields a corresponding classification algorithm is which can cut down the encoding timeinto second’s magnitude. results suggest that the algorithm can balance the overall encodingperformance efficiently, that is, with a higher speed and a better PSNR gain.