The advantages of using signals in digital domain are of many folds. Some of the advantages compared to the analog signals are multiplexing, storage, compression & ease of reproduction of digital signals. Added to this the Moore's law factor, the cost of digital hardware continues to halve every two years while performance or capacity doubles over the same period has led to an exponential use of devices that are digital in nature. Digital signals are obtained by sampling & quantizing the analog signal so that they can be efficiently represented. In this paper, different kinds of waveform coding techniques such as DPCM & ADPCM are studied. Performance is evaluated based on Signal to Quantization Noise Ratio (SQNR) & Mean square error (MSE) measures. Encoding & decoding complexity as a function of time is also studied.

1. N. Jayant & P. Noll, Digital Coding of Waveforms: Principles & Applications to Speech & Audio, Prentice Hall, USA, 1984.
2. B. P. Lathi & Zhi Ding, Modern Digital & Analog Communication Systems, Oxford University Press, International Fourth Edition, 2010.
3. Simon Haykin, Communication Systems, John Wiley & Sons, Fourth Edition, 2000.
4. K. Sayood, Introduction to Data Compression, Harcourt India Pvt Ltd & Morgan Kaufmann Publishers, 1996.
5. P. Cumminskey, N. S. Jayant & J. L. Flanganan, Adaptive quantization in differential coding of speech, The Bell System Technical Journal, Vol. 52, No. 7, September 1973, pp 1105-1118.
6. N. S. Jayant, Adaptive quantization with a one-word memory, The Bell System Technical Journal, Vol. 52, No. 7, September 1973, pp 1119-1144.
7. Vinay K. Ingle & John G. Proakis, Digital Signal processing using MATLAB, Cengage learning, Third Edition, 2010.

Dpcm Adpcm Sqnr Mse Predictor Adaptive Quantizer