Efficient context-based entropy coding for lossy wavelet image compression

We present an adaptive image coding algorithm based on novel backward-adaptive quantization/classification techniques. We use a simple uniform scalar quantizer to quantize the image subbands. Our algorithm puts the coefficient into one of several classes depending on the values of neighboring previously quantized coefficients. These previously quantized coefficients form contexts which are used to characterize the subband data. To each context type corresponds a different probability model and thus each subband coefficient is compressed with an arithmetic coder having the appropriate model depending on that coefficient's neighborhood. We show how the context selection can be driven by rate-distortion 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 efficient 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.