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TNS represents a solution to overcome this problem by shaping the quantization noise in the time domain according to the input transient.
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These methods mainly exploit the masking property of the human auditory system for shaping the quantization noise so that it will be inaudible.
The purpose of the noise-shaping filter is to shape the quantization noise appropriately in the frequency domain [21].
Since the inverse TDLP ARR) filter follows the spectral impulses for tonal signals, it shapes the quantization noise according to the input signal.
By oversampling the signal, a certain amount of redundancy is introduced, and by proper noise shaping of the quantization noise a trade-off between the reconstruction qualities due to receiving different subsets of descriptions is possible, cf. Figure 1.
Although the overall quantization noise may not be changed by the noise shaping, the SQNR is increased in the information signal frequency band of the frequency spectrum.
Alternatively, shape the clay by hand.
Further, the NDM is improved by weighting the quantization errors.
The -factor was 200. 8 × 8 DCT coefficients are divided by the quantization step, which is generated by, where, and denotes a predefined quantization table and a -factor, resp).
The main difference are the second moment of the shaping lattice used for the quantization, (sigma ^{2}(Lambda _{Q})= 1+D+frac {g^{2}(K-1)P+g^{ K-1 P+1}((K-1)P+1)}{ K-1 P+1}).
where represents the th quantization interval, determined by the quantization step and an offset angle.
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