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Vector quantization for entropy coding of image subbands

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2 Author(s)
Senoo, T. ; Media Lab., MIT, Cambridge, MA, USA ; Girod, B.

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

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Image Processing, IEEE Transactions on  (Volume:1 ,  Issue: 4 )