. To transmit the sample values via a digital system, we have to represent each sample value in numerical form. This requires quantizing where each accurate sample value is rounded off to the closest numerical value in a set of digital words in use. Figure 3.9 represents the original and the quantized signal. The latter stays at the sample value until the next sam- pling instant. +127 max: Quantized waveform Original signal -127 max: Accurate value Quantized value Quantizing error Quantizing error Error curve, quantizing noise Figure 3.9 Quantizing and noise. Signals Carried over the Network In this quantizing process the information in accurate signal values is lost because of rounding off and the original signal cannot be reproduced exactly any more. The quality of the coding depends on the number of quan- tum levels that is defined to provide the required performance. The more quantum levels we use, the better performance we get. For example, for a voice signal 256 levels (8-bit binary words) are adequate, but for music encoding (CD recording) 65,536 levels (16-bit binary word) are needed to give sufficient performance. In the case of binary coding, the number of quantum levels is q = 2n, where q denotes the number of quantum levels and n is the length in bits of the binary code words that describe the sample values. The better quality we require, the more quantum levels we need and the longer sample words we have to use. This leads to the requirement of a higher bit rate for transmission of the data representing the original message. The data rate must be SO high that the digital word of the previous sample will be transmitted before the next one is available for transmission. In each system, a certain compromise has to be made between quality and the data rate. In uniform quantizing, the quantum levels are uniformly spaced between certain minimum and maximum values of the analog signal. In the next section we consider quantizing noise that the rounding off produces in the case of uniform quantizing. 3.6.3 Qu