Image compression using lattice vector quantization with code book shape adapted thresholding

To improve lattice vector quantization (LVQ) performance in image compression applications, we propose to design a new code book shape adapted thresholding. As for the scalar dead zone quantizer, the goal here is to remove non significant data, but in our case the n-dimensional dead zone permits the exploitation of the characteristics of source vectors (i.e. blocks of wavelet coefficients). A theoretical rate model is defined for this new truncation shape. It permits tuning of the dead zone parameters in order to reach a minimum of distortion of the quantized source at a given rate, and thus to outperform classical LVQ.