Image subband coding using context-based classification and adaptive quantization

Adaptive compression methods have been a key component of many of the recen tly proposed subband (or wavelet) image coding techniques. This paper deals with a particular type of adaptive subband image coding where we focus on the image coder's ability to adjust itself "on the fly" to the spatially va rying statistical nature of image contents. This backward adaptation is dis tinguished from more frequently used forward adaptation in that forward ada ptation selects the best operating parameters from a predesigned set and th us uses considerable amount of side information in order for the encoder an d the decoder to operate with the same parameters. Specifically, we present backward adaptive quantization using a new context-based classification te chnique which classifies each subband coefficient based on the surrounding quantized coefficients. We couple this classification with online parametri c adaptation of the quantizer applied to each class. A simple uniform thres hold quantizer is employed as the baseline quantizer for which adaptation i s achieved. Our subband image coder based on the proposed adaptive classifi cation-quantization idea exhibits excellent rate-distortion performance, in particular at very low rates. For popular test images, it is comparable or superior to most of the state-of-the-art coders in the literature.