100211-lecture4 - EE522 Communications Theory Spring 2010...

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1 EE522 Communications Theory Spring 2010 Instructor: Hwang Soo Lee Lecture #4 – Non-Uniform Quantization using the Lloyd Max Algorithm
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2 Announcements ± Handouts: ² Course Notes #4
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3 Block Diagram of Digital Communications
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4 Digital Representation of Analog Signals ± Analog signals (e.g. voice,video) are continuous in time and amplitude:
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5 Digital Representation of Analog Signals ± Sampling analog signals makes them discrete in time:
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6 Digital Representation of Analog Signals ± Quantization of sampled analog signals makes the samples discrete in amplitude:
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7 Quantization ± Source coding compresses digital data with no loss of information. ± Continuous -valued samples of data require an infinite # of bits to represent with perfect precision. ± Quantization is the process of approximating continuous -valued samples with a finite # of bits. ± Quantization always introduces some distortion. ± Source coding may be performed after quantization (sometimes called entropy coding).
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8 Notation Associated with Quantization ± Let X be a random variable representing a sample of data. ± Then is the quantized value of X. ± A quantizer has L quantization levels: ± The endpoints of the quantization regions are specified by L+1 values: , where ± Then:
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9 Example Quantizer
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10 More Notation and Terminology ± The rate
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This note was uploaded on 11/23/2010 for the course EE EE522 taught by Professor Eeehwangsoo during the Spring '10 term at Korea Advanced Institute of Science and Technology.

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100211-lecture4 - EE522 Communications Theory Spring 2010...

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