Context quantization with Fisher discriminant for adaptive embedded wavelet image coding

Recent progress in context modeling and adaptive entropy coding of wavelet coefficients has probably been the most important catalyst for the rapidly maturing area of wavelet image compression technology. In this paper we identify statistical context modeling of wavelet coefficients as the determining factor of rate-distortion performance of wavelet codecs. We propose a new context quantization algorithm for minimum conditional entropy. The algorithm is a dynamic programming process guided by Fisher's linear discriminant. It facilitates high-order context modeling and adaptive entropy coding of embedded wavelet bit streams, and leads to superb compression performance in both lossy and lossless cases.