Wavelet Image Compression Based on Context Predicting With Past Information in Rows (Columns)

In order to exploit correlation between bits of wavelet coefficients as much as possible, this paper presents a new algorithm called wavelet image compression based on context predicting with past information in rows (columns) on the thought of context-based coding. In the selection of high-order context, this paper presents seven points in three rows (columns) context using mutual information as a theoretical basis. In the prediction and quantization of high-order context, we make use of mathematical tools, such as Fisher discriminant dynamic programming, etc., to obtain a good conditional probability estimate of significance and sign bits of wavelet coefficients. The experiments show that reasonable context modeling of the presented algorithm leads to satisfying results. This paper makes some valuable contributions into finding theoretical bases for wavelet image compression.