Quantization

[attachment=17044]
This involves representing the sampled data by a finite number of levels based on some criteria such as minimization of the quantizer distortion, which must be meaningful.

Quantizer design includes input (decision) levels and output (reconstruction) levels as well as number of levels. The decision can be enhanced by psychovisual or psychoacoustic perception.

Quantizers can be classified as memoryless (assumes each sample is quantized independently) or with memory (takes into account previous sample) .

Alternative classification of quantisers is based on uniform or non- uniform quantization. They are defined as follows.


Max Lloyed Quantizer

In this case quantizers are designed minimizing MSQE and tables are developed for input governed by standard distribution functions such as gamma, Laplacian, Gaussian, Rayleigh, Uniform.

Quantizers can also be designed tailored to histograms. For a well designed predictor, the histogram of predicted errors tend to follow Laplacian distribution.