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Makhoul, S. Roucos, and H. Gish, Vector quantization in speech coding, Proc. IEEE, 73: 1551–1588, 1985. 14. A. Gersho and R. M. Gray, Vector Quantization and Signal Compression, Norwell, MA: Kluwer, 1992. 15. P. A. Chou, T. Lookabaugh, and R. M. Gray, Entropy-constrained vector quantization, IEEE Trans. , 37: 31–42, 1989. 16. J. Foster, R. M. Gray, and M. O. Dunham, Finite-state vector quantization for waveform coding, IEEE Trans. Inf. Theory, 31: 348–359, 1985. 17. R. L. Baker and R. M. Gray, Differential vector quantization of achromatic imagery, Proc.
Vector quantization is one of the most popular lossy data compression techniques. It is widely used in image, audio, and speech compression applications. The most popular vector quantization is fixed-length vector quantization. In the quantization process, consecutive input samples are grouped into fixed-length vectors first. As an example, we can group L samples of input speech as one L-dimensional vector, which forms the input vector to the vector quantizer. For a typical vector quantizer, both the encoder and the decoder share a common codebook, C ϭ ͕ci; i ϭ 1, .
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