More exact conditions for when the quantization noise is uniform have been presented in [23]. For the general case using a DAC or ADC with q bits, we have a discrete set of 2q possible outputs from the quantizer. Most commonly, uniform mid-rise quantizers are assumed; however, other ...
8.1.1 Uniform Quantization In most practical communication systems, quantization is assumed to be a memoryless nonlinear process with many-to-one analog to discrete signal mapping. In reality, however, the quantization noise is made up of a large number of spurious signals. Since most of the spu...
000) divided by the number of bins (101). In other words, the noise amplitude is uniformly distributed between ±LSB/2. If we increase the quantizer resolution, we’ll get an even more uniform amplitude distribution. This
This has often been misinterpreted as saying that the quantized random variable can be approximated as being the input plus signal-independent uniform noise, a clearly false statement since the quantizer error is a deterministic function of the signal. The “bandlimited” property of the ...
Noise in Sampling.- 3.4 Practical Sampling Schemes.- 3.5 Sampling Jitter.- 3.6 Multidimensional Sampling.- 3.7 Problems.- 4 Linear Prediction.- 4.1 Introduction.- 4.2 Elementary Estimation Theory.- 4.3 Finite-Memory Linear Prediction.- 4.4 Forward and Backward Prediction.- 4.5 The Levinson-Durbin ...
In the absence of channel noise, variable-length quantizers perform better than fixed-rate LloydMax quantizers for any source with a non-uniform density function. However, channel errors can lead to a loss of synchronization resulting in a propagation of error. To avoid having variable rate, ...
The signal-to-noise ratio is also affected by quantization. Some special topics are the effect of dither and the relation between differential nonlinearity and signal-to-noise.This is a preview of subscription content, log in via an institution to check access. Similar...
Smooth regions may be considered smooth even when interrupted by small areas of noise, film grains, or other color variations. FIG. 16 is a flow chart showing a generalized technique 1600 for applying differential quantization based on texture information. An encoder such as the encoder 1000 of ...
Many successful deep distribution models in deep learning learn a density, i.e., the distribution of a continuous random variable. Na\"ive optimization on discrete data leads to arbitrarily high likelihoods, and instead, it has become standard practice to add noise to datapoints. In this paper...
D. Gibson, "Explicit additive noise models for uniform and nonuni- form MMSE quantization," Signal Processing, vol. 7, pp. 407-414, 1984.K. Sayood and J. D. Gibson, "Explicit additive noise models for uniform and nonuniform MMSE quantization," Signal Processing, vol. 7, pp. 407-414,...