Sphere-packings, lattices, and groups
Sphere-packings, lattices, and groups
Vector quantization and signal compression
Vector quantization and signal compression
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Lattice vector quantization schemes offer high coding efficiency without the burden associated with generating and searching a codebook. The distortion associated with a given lattice is often expressed in terms of the G number, which is a measure of the mean square error per dimension generated by quantization of a uniform source. Subband image coefficients, however, are best modeled by a generalized Gaussian, leading to distortion characteristics that are quite different from those encountered for uniform, Laplacian, or Gaussian sources. We present here the distortion associated with Z, E/sub 8/, and Leech lattice quantization for coding of generalized Gaussian sources, and show that for low bit rates the Z lattice offers both the best performance and the lowest implementational complexity.