On entropy coded and entropy constrained lattice vector quantization

Two lattice vector quantization methods are compared. The first, classical method uses Dirichlet domains of lattice points as quantization cells and assigns them reconstruction vectors minimizing the distortion. The second method uses the lattice as codebook but modifies the shapes of the quantization cells by searching for each input vector the mapping onto either the nearest lattice points or one of its neighbors, such that an error criterion subject to an entropy constraint is minimized. Both methods use entropy coding for the codevector indices and an entropy constrained global optimization scheme to find the best lattice scale. Examples demonstrate that the rate-distortion performances of both methods are nearly identical. Hence, especially for non-stationary sources the selection of one of these methods should be based on other criteria: while the first method requires the transmission of the new codebook, the decoder for the second method can permanently adapt itself without side information. The drawback is a higher search complexity for encoding