Visual quantization When groups of neighbouring pixels are quantized to the same value, regions of constant gray levels are formed, whose boundaries are called “contours”. Uniform quantization of common images where pixels represent luminance, require 8 bits (i.e. 256 gray levels).

The HSI Color Model HSI stands for hue, saturation, intensity. This model is interesting because it can initially seem less intuitive than the RGB model, despite the fact that it describes color in a way that is much more consistent with human visual perception.

In the quantization stage, the image creation device must use a table of values known as the quantization tables. Each table has 64 values that range from 0 to 65,535. 1. A lower number means that less data will be discarded in the compression and a higher quality image should result.

The quantization parameter (QP), the ratio of I, P, and B frames and the target bit-rate (in case of rate-control coding) are the most important ones having strong effect on the quality of the decoded video.

Quantization is the process of converting a continuous range of values into a finite range of discreet values. This is a function of analog-to-digital converters, which create a series of digital values to represent the original analog signal.

Optimal quantization, a fundamental problem in source coding and information theory, can be formulated as a discrete optimization problem. In 1964 Bruce (“Optimum Quantization,” Sc. thesis, MIT, May 1964) devised a dynamic programming algorithm for discrete optimal quantization.

A promising alternative to reduce the energy consumption is 1-bit quantization. In this context, oversampling with respect to the symbol duration is promising because CPM signals are not strictly bandlimited and because it reduces the loss in achievable rate caused by the quantization.

1. Calculate the ratio of quantization levels (32 in your case) to the value range (-4 to 4, so 8 in your case. Result would be 32/8=4.
2. Multiply the sequence with that ratio.
3. Add 0.5.
4. Convert to integer using “truncation” (which is the default behavior for most languages)

Answer : Quantization error is the difference between the analog signal and the closest available digital value at each sampling instant from the A/D converter. Quantization error also introduces noise, called quantization noise, to the sample signal.

Explanation: The quantisation error depends on the number of bits which is used to represent the analogue vale. 3. Which is the first type of error caused during the conversion process? Explanation: The quantisation error is the first type of error caused in the conversion process.

So how can a data-acquisition system reduce quantization errors? Because these errors depend only on an ADCs resolution, sampling at a much higher rate than you would normally spreads the quantization noise over a larger bandwidth. And thus the power density for a fixed bandwidth decreases as fsample increases.

This error is called quantization error (Vq) and can be calculated by subtracting the ADC input (Vin) from the output of the DAC (Vout) as shown in Figure 3 below.