Elec 540: Source Coding and Compression
Spring 1999 - Rice University
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Instructor
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Aria Nosratinia, 2019
Duncan Hall, Ext. 5056, aria@ece.rice.edu
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Time
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MWF 10-11am
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Place
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Room 1075 Duncan Hall
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Textbook
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Gersho & Gray, Vector Quantization and Signal Compression, Kluwer
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Grading
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Two midterms, one final exam, homeworks
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Prerequisite
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One semester of advanced random processes required. One-semester background in
Information theory recommended (discuss with instructor).
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Office Hours
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TBA
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Efficient communication and storage of information is critical to all
aspects of science and technology, especially engineering. Signals, in
their original form, are analog and their description in general
requires an infinite amount of information. To reduce the requirements
on communication bandwidths (or alternatively, the length of storage
media), signals are represented in approximate (quantized) form.
This course explores the theory and practice of quantization and
compression of signals. Source coding is part of the general theory of
communication, and is closely related to proability, random processes,
and information theory, as well as signal processing.
Contents:
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Overview of Information Theory
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Information and entropy
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Important inequalities in information theory
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Asymptotic Equipartition Property (AEP)
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Introduction to rate-distortion theory
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Entropy coding
- Prefix codes
- Huffman coding
- Arithmetic coding
- Ziv-Lempel codes
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Scalar Quantization
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Encoder-decoder decomposition of quantizers
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Performance of scalar quantizers
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Asymptotic theory, Bennet Formulae
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Optimality conditions
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Properties of optimal quantizers
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The Lloyd-Max algorithm
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Vector Quantization
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Structural properties
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Performance
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High resolution theory
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Optimality conditions
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Implications of optimality
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The generalized Lloyd algorithm
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Entropy-constrained VQ
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Drawbacks: delay, complexity, and curse of dimensionality
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Predictive Coding
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Overview of linear estimation theory
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Overview of linear prediction
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Fundamental theorem of predictive quantization
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Performance bounds
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Transform Coding
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Motivation
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The bit allocation problem
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Optimality and the Karhunen-Loeve transform
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Performance gains
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Other transforms
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Extensions of Vector Quantization
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Tree-structured VQ
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Classified VQ
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Other constrained VQ
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Scalar Vector Quantization
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Trellis Coded Quantization
Aria Nosratinia
Last modified: January 1999