网络信道的不稳定性可能会造成数据传输出错，从而可能导致恢复图象质量的急剧下降，或者使算法失效.针对这种情况，提出了一种新的图象压缩方法来改善上述问题.该方法是首先将图象进行小波分解，然后针对不同的频带特征采用不同的压缩方法进行编码.在图象编码中，对于低频子带系数采用 DPCM编码，对于高频子带系数则采用多描述标量量化器.由于小波分解后的系数经活动性预测分类后具有拉普拉斯分布的性质，因此可对其采用预测分类的自适应量化方法进行编码.同时由于不同子频带的系数之间具有不同的相关性，因此在编码过程中采用了不同的方法来分别对高频子带系数和低频子带系数进行编码，并且充分利用了频带系数分布具有拉普拉斯分布的特点.实验表明，该方法在减少传输误码敏感性方面具有理想的效果.

The instability of network channels will lead to transmission errors which deteriorate the quality of restored image, sometimes even make compression algorithms invalid. In this paper, a new image compression method is porposed to reduce such severity. Firstly a wavelet transform of the image is taken to obtain wavelet subbands of the image. According to different characteristics of each subband, different compression methods are applied to it. DPCM(Differential pulse code modulation) is employed to lowest frequency subband and multiple description scalar quantizer(MDSQ) to high frequency subband. Because coefficients match Laplacian distribution after classification based on their acitivity prediction, a context based classification and adaptive quantizer(CBCAQ) is used to them. Because there are different correlations in different subband, different compression methods are used to encode high frequency and low frequency coefficients. And during encoding process, Laplacian distribution characteristics of subband coefficients are fully exploited. The results of experiments show that the proposed method of image compression performs well in reducing transmission errors.