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In the method, the normalized host signal vector is constructed for quantization.
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The noise detection thresholds of coefficients in each color component are derived in a locally adaptive fashion based on the wavelet decomposition, by which perceptually significant coefficients are selected and a perceptually lossless quantization matrix is constructed for embedding watermarks.
Lam and Reibman[7] constructed optimal quantization partitions in distributed estimation systems where the necessary conditions for the partitions were presented.
To construct regular quantization partitions, we suggest the average distance error as a metric in the functional quantization since the distance is monotonic in each sensor reading.
It should be noticed that the regular quantization partitions can be also constructed by the functional quantization of a monotonic estimator (e.g., a linear minimum mean square error (MMSE) estimator) since each quantization partition for the estimator transforms to a regular one for quantizers at each sensor due to its monotonicity in each sensor reading[9].
The normalized dither modulation (NDM) is presented, which works by constructing a gain-invariant vector with zero mean for quantization.
The main idea of NDM is to construct a gain-invariant vector with zero mean for quantization.
If the quantized range defined by the threshold is used for quantization, then the ERR and EAR obtained using this method will have no impact.
For digital control systems, and under the stronger assumption of incremental input-to-state stability, symbolic models can be constructed through a suitable quantization of the inputs.
In order to compensate for quantization errors, an adjustment range of quantization sensitivity for a dynamic uniform quantizer is given through the flexible choices of design parameters.
Detail operations for quantization are described in this paper.
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