In many fields such as remote sensing or medicine

lossless compression is usually required because of the demand for high image quality. A lossless image compression algorithm is proposed in this paper considering the limitation of existing methods. The algorithm takes advantage of the effects of the distribution of pixels on compression ratio. The pixels are classified firstly

and the classification results are recorded in mask images. After that

the pixels of each class are re-ordered using the Hilbert curve. Finally the mask image and the data stream of each class are coded using the LZW algorithm. Experiment was conducted on both standard testing images and remote sensing images

and the result showed that the proposed algorithm produced higher compression ratio than the methods of LZW

RLE and Huffman. The proposed algorithm is also easy to implement.