DPCM encodes the PCM values based on

1. quantization level
2. difference between the current and predicted value
3. interval between levels
4. none of the mentioned

Solution

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Correct option is B. difference between the current and predicted value

Explanation: Differential Pulse Code Modulation (DPCM) is a technique used to encode PCM (Pulse Code Modulation) values based on the difference between the current and the predicted value of the signal. DPCM aims to reduce the amount of data required to represent the signal by exploiting the correlation between consecutive samples. By encoding the differences instead of the absolute values, DPCM can often achieve more efficient data compression.

Example: Consider a simple DPCM system that predicts the next sample value based on the previous one. Suppose the current sample value is 10 and the previous sample value is 8. The predicted value for the current sample would also be 8. The difference between the current and predicted value is 10 – 8 = 2. In this case, DPCM would encode the difference (2) rather than the absolute value (10).

Facts:

• DPCM is a lossy compression technique, as the quantization error introduced during encoding can result in a loss of information when decoding the signal.
• DPCM can be used in various applications, such as audio and image compression, where consecutive samples are often highly correlated.

Relevant Stats: The performance of DPCM depends on the accuracy of the prediction algorithm and the quantization of the differences. A more accurate prediction algorithm will result in smaller differences that can be more efficiently encoded. Depending on the application, DPCM can achieve significant data compression compared to standard PCM, potentially reducing the required bitrate and storage space for the encoded signal.

In summary, DPCM encodes the PCM values based on the difference between the current and predicted value of the signal. By exploiting the correlation between consecutive samples, DPCM can achieve more efficient data compression than standard PCM.

The performance of DPCM depends on the accuracy of the prediction algorithm and the quantization of the differences. This technique is widely used in various applications, including audio and image compression, where consecutive samples often exhibit a high degree of correlation.