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.