Vector quantization for entropy coding of image subbands

Vector quantization for entropy coding of image subbands is investigated. Rate distortion curves are computed with mean square error as a distortion criterion. We show that full search entropy constrained vector quantization of image subbands results in the best performance, but is computationally expensive. Lattice quantizers yield a coding efficiency almost indistinguishable from optimum full-search entropy-constrained vector quantization (EC-VQ). Orthogonal lattice quantizers were found to perform almost as well as lattice quantizers derived from dense sphere packings. An optimum bit allocation rule based on a Lagrange multiplier formulation is applied to subband coding. Coding results are shown for a still image.