Differential Pulse code Modulation In PCM, each sample of the waveform is encoded independently of all the other samples. However, most source signals including speech sampled at the Nyquist rate or faster exhibit significant correlation between successive samples. In other words, the average change in amplitude between successive samples is relatively small. Consequently an encoding scheme that exploits the redundancy in the samples will result in a lower bit rate for the source output.
Differential Pulse code Modulation(Contd) A relatively simple solution is to encode the differences between successive samples rather than the samples themselves. The resulting technique is called differential pulse code modulation (DPCM). Since differences between samples are expected to be smaller than the actual sampled amplitudes, fewer bits are requrired to represent the differences. In this case we quantize and transmit the differenced signal sequence e(n)= s(n)- s(n - 1), where s(n) is the sampled sequence of s(t).
Adaptive Differential Pulse Code Modulation ADPCM (adaptive differential pulse-code modulation) is a technique for converting sound or analog information to binary information (a string of 0's and 1's) by taking frequent samples of the sound and expressing the value of the sampled sound modulation in binary terms. ADPCM is used to send sound on fiber-optic long-distance lines as well as to store sound along with text, images, and code on a CD-ROM.
Delta Modulation • In Delta Modulation, an incoming message signal is over sampled (i.e., at a rate much higher than Nyquist rate) to purposely increase the correlation between adjacent samples of the signal. This is done to permit the use of a simple quantizing strategy for constructing the encoded signal. • DM provides a approximation to the sampled version of the message signal. The difference between the input and the approximation is quantized into only two levels, namely + Δ or - Δ signals, corresponding to positive and negative differences. Thus if the approximation falls below the signal at any sampling epoch, it is increased by Δ and vice versa.
Adaptive Delta Modulation • To overcome the quantization errors due to slope distortion and granular noise, the step size (δ) is made adaptive to variations in the input signal x(t). In the steep segment of the signal x(t), the step size is increased. When the input is varying slowly, the step size is increased. This method is called Adaptive Delta Modulation. • Adaptive DM has certain advantages over DM. They are, • The signal to noise ratio is better than ordinary DM because of the reduction in slope overload distortion and granular noise. • Because of the variable step size, the dynamic range of ADM is wide. • Utilization of bandwidth is better than DM.