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In this paper, we propose a context-based inverse quantization and show its application in wavelet image compression. The proposed method breaks the traditional one-to-one mapping of the quantization index to reconstruction value in inverse quantization while maps an index to several different reconstruction values according to the corresponding contexts of the index. By accurate context modeling, this method can reduce the quantization distortion significantly. Since the quantization indices used for encoding is not changed, this method does not increase the encoding bit rates except the negligible overhead.