Scalar quantization using vector measure with application to quantization of LSF parameters.
Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference(1996)
摘要
Vector quantization (VQ) is usually adopted to quantize parameters of multiple dimensions. It uses certain meaningful vector measures for codevector selection and codebook training to achieve bit reduction, at the expense of, however, higher computation and memory requirement. Due to the aforementioned shortcomings, in practical applications, scalar quantization (SQ) is still applied to quantize each of the multidimensional parameters individually. In this paper, we propose to apply a vector measure method to the table lookup in scalar quantization so as to improve its coding efficiency without increasing storage requirement. Two LSF scalar quantizers are investigated in which the weighted Euclidean distance is used as a vector measure to improve the performance of both quantizers.
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关键词
lsf parameter,scalar quantization,certain meaningful vector measure,bit reduction,vector quantization,aforementioned shortcoming,memory requirement,vector measure method,vector measure,lsf scalar quantizers,storage requirement,euclidean distance,linear predictive coding,speech coding,multidimensional systems,coding efficiency,frequency
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