Indexing and entropy coding of lattice codevectors

We present two methods of entropy coding for the lattice codevectors. We compare our entropy coding methods with one method previously presented in the literature from the point of view of rate-distortion as well as of the computation complexity and memory requirements. The results are presented for artificial Laplacian and Gaussian data, as well as for LSF parameters of speech signals. In the latter case, the multiple scale lattice VQ (MSLVQ) is used for quantization, which reduces the rate gain of the entropy coding method when compared with the fixed rate case, but allows a dynamic allocation of the bits in the whole speech coding scheme.