The basic principle of Compressed Sensing (CS) theory is that if a signal is sparse, CS promises to deliver a full recovery of this signal with high probability from far fewer measurements than the original signal. Unfortunately, image signals usually are not sparse, and thus it is difficult to obtain high compression performance for image compressed sensing.This paper proposed a simple and efficient zerotree compressed sensing method for images. In the proposed scheme, the classical zerotree coding is integrated into the process of measure to encode the precise locations of significant elements, which is used to restore the original image by the proposed pursuit reconstruction algorithm to improve the quality of the reconstructed image. The experimental results show that, compared with the existing matching pursuit algorithms and Embedded Zerotree Wavelet (EZW) coding algorithm, the proposed algorithm achieves much higher compression ratio and better image quality.