title: Vector quantization for entropy coding of image subbands.
abstract: Vector quantization for entropy coding of image subbands is investigated. Rate distortion curves are computed with mean square error as a distortion criterion. The authors 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. 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.
authors.affiliation: Media Lab., MIT, Cambridge, MA.
authors.forename: T
authors.initials: T
authors.lastname: Senoo
authors.forename: B
authors.initials: B
authors.lastname: Girod