在遥感、医学等许多应用领域中，出于对图像质量的要求，图像一般必须进行无损压缩。针对现有方法的局限，提出了一种无损压缩算法。该算法利用图像灰度分布对压缩比的影响，首先对像素进行灰度分类并用掩膜图记录类别信息，然后采用Hilbert曲线将各类像素进行块间和块内重排，最后采用LZW(lempel-ziv-welch)算法对掩膜图与各类像素的数据流进行编码。经过对多幅标准测试图像以及遥感图像的实验结果表明,本文算法在总体上具有比LZW、行程编码RLE(run length encoding)和霍夫曼(Huffma

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.