Abstract
In this work, a high-quality, space-frequency adaptive, subband image codec is presented. The algorithm jointly optimizes space and frequency segmentation of an image. First, wavelet packets are formed to localize the high-energy frequency bands. Then, the subband coefficients are further classified to maximize the coding gain. The design target is the minimization of Lagrangian cost functions: the cost is the distortion in l/sup 2/ norm and the constraint is the coding rate. The resultant mapping is used to quantize the subband coefficients with trellis coded quantization. This is followed by adaptive arithmetic coding producing the final compressed bitstream. The described approach is tested on several images, and the results are compared to some other compression techniques.
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