Abstract
In the past three or so years, particularly during the JPEG 2000 standardization process that was launched last year, statistical context modeling of embedded wavelet bit streams has received a lot of attention from the image compression community. High-order context modeling has been proven to be indispensable for high rate-distortion performance of wavelet image coders. However, if care is not taken in algorithm design and implementation, the formation of high-order modeling contexts can be both CPU and memory greedy, creating a computation bottleneck for wavelet coding systems. In this paper we focus on the operational aspect of high-order statistical context modeling, and introduce some fast algorithm techniques that can drastically reduce both time and space complexities of high-order context modeling in the wavelet domain.
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