Jointly optimal classification and uniform threshold quantization in entropy constrained subband image coding

A method for coding a source modeled by an infinite Gaussian mixture distribution is proposed. The source is first split into N classes. The samples of each class are then quantized by an infinite-level uniform threshold quantizer followed by an entropy coder designed for each class. The problem of joint optimization of this system´s rate distortion performance is first solved theoretically, assuming an exponential mixing density. A comparison to a system optimal for high rates, using one common quantizer for all classes, showed that for a fixed distortion the rate was reduced by 11-12% at low rates for a fixed N=5. A subband image coder, using the optimum theoretical parameter values was simulated. The resulting coder has high performance and low complexity