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However, an analytical expression to which the MSE of the optimal PQ converges as the quantizer rate R grows can be found noting the fact that, as R→∞ (that is, as the size of the maximum quantization interval approaches zero), the closed-loop prediction error for a Gaussian source is also Gaussian and therefore the quantizer MSE approaches the Gish-Pierce asymptotic [14].
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The MOS-LQO scores are firstly quantized with a maximum quantization error of 0.025.
The quantization interval of the static quantizer part is strongly related to the data size.
The azimuths on the circle in the quantization interval region will be quantized as the inside quantization codeword.
That means the azimuths in each quantization interval region will be quantized as the quantization codeword inside the region.
We assume a uniform quantizer with saturation levels L=2 and quantization interval d=2.
The dashed line indicates the maximum quantization error of the 3-bit quantizer, which is 1/8.
The dashed line in the figure indicates the maximum quantization error when CPD1 was directly quantized using 3 bits.
When one designs a quantizer for a given source, the size of the quantization interval depends on the variance of the input.
The dashed lines again indicate the maximum quantization error that can be obtained when the CPD1 was directly quantized.
where is the quantization interval.
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