As mentioned earlier, the error in the signals may result from various noise sources, such as thermal noise, channel interference,
quantization error, and systemic sample phase mismatch.
Normalization of the
quantization error squared is expressed as
Two mode-dependent quantizers are set to balance the network congestion and
quantization error. A Markov process is used to model the time delay which is used to describe the quantization density as a function.
Second, we propose the parity checks strategy to eliminate
quantization error. Third, if the transmitted cipher image is not polluted by noises, the embedding rate achieves 2 bpp.
Disparity
quantization error is reduced using multi fitting algorithm to obtain the effective and consistency in disparity mapping.
In these cases, the NGN network can be useful for 3D modeling since their average
quantization error is below this range.
The
quantization error, or noise, is then defined as
Every peak of the spectrum corresponding to every incidence signal usually contains multiple consecutive nonzero amplitudes because of
quantization error of [PHI].
Although the results showed that the detection accuracy of the SVI system was high, surpassing 94%, in order to make the system to be more rapid, general and useful, further studies need to do: not only reducing the
quantization error through increasing the resolution of seed images, but also lowering the amount of system calculation to save the computing time.
Therefore, the uncertainty of time measurement can be determined by
quantization error only.