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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