With the increasing volumes of remote sensing data

data compression is receiving more and more attention. Adapting to the specialities of remote sensing data the low local correlation and the rich complex texture information

this paper presents an adaptive scalar vector hybrid quantization method for compression based on wavelet transform. According to textural intensity of every block in wavelet transformed high frequency subimage

we classify them into four classes. Compressing the plain block is at high compression ratio

and the textural block at high fidelity

The method enable the balance of the restore error of every block. This method is time efficient by avoiding the codebook training and searching

while surpass the performence of JPEG for single image. By combining with K L transformation

which is a kind of correlation reduction methods

we apply the presented method to multi band remote sensing image

and good results have been obtained.