Eecient Context-based Entropy Coding for Lossy Wavelet Image Compression
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چکیده
In this paper we present an adaptive image coding algorithm based on novel backwardadaptive quantization/classi cation techniques. We use a simple uniform scalar quantizer to quantize the image subbands. Our algorithm puts each coe cient into one of several classes depending on the values of neighboring previously quantized coe cients. These previously quantized coe cients form contexts which are used to characterize the subband data. To each context type corresponds a di erent probability model and thus each subband coe cient is compressed with an arithmetic coder having the appropriate model depending on that coe cient's neighborhood. We show how the context selection can be driven by ratedistortion criteria, by choosing the contexts in a way that the total distortion for a given bit rate is minimized. Moreover the probability models for each context are initialized/updated in a very e cient way so that practically no overhead information has to be sent to the decoder. Our results are comparable or in some cases better than the recent state of the art, with our algorithm being simpler than most of the published algorithms of comparable performance.
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تاریخ انتشار 1997