At the coarse level disparity estimation, most methods suffer from disparity

quantization errors, which cause wrong initialization for the next level, t he boundary blurring is inherent to the multiresolution approaches, which is deteriorated by up sampling processes, which produce better final disparity from multiscale processing, a fusion method that combines multiple intermediate estimates is required.

In these cases, the NGN network can be useful for 3D modeling since their average

quantization error is below this range.

In the case of hardware wavelet-based systems, it is desirable that the

quantization error ([q.

Quantization error (Je), The maximum intra-distances (dmax), The minimum inter-distances (dmin) generally are used to analyze and take ideas on segmentation methods

In ND application mean value should be evaluated to achieve suppression of

quantization error.

Therefore, the performance comparison and the effects of

quantization error for the three data formats are referenced to the estimated speed profile they produce.

Researchers at NIST have developed a new technique for modeling and implementing a method for minimizing the

quantization errors that often accumulate in electronically generated time bases.

For every input [alpha] + [beta] (i + 1/2), both [alpha] + [beta]i and [alpha] + [beta] (i + 1) minimize the

quantization error, and a complete model must provide some basis for choosing between these alternatives.

Subjective perception of

quantization error greatly varies with the frequency and it is advantageous to use coarser quantizers for the higher frequencies.

As a result, there is essentially no

quantization error or non-monotonic behavior, and temperature hysteresis is very low, typically less than 2 mg over the entire 40x C to +175x C temperature range.

The dynamic range of the assumed variance is divided into sub-ranges, and for each of them is designed a separate quantizer whose parameters are set so that the

quantization error is minimal [2]-[4],

Here, note that q(u(t)) = u(t) + [phi](t), where the kth element of the

quantization error [phi](i) satisfies

Quantization of the relative coordinates induces

quantization error, see Figure 2.

One noise source at -53 dBm/Hz would provide amplitude and phase tracking calibration, and the other at -143 dBm/Hz would randomize the A/D

quantization error.

This process necessarily introduces some round-off or

quantization error.