ECE Course Syllabus
ECE6260 Course Syllabus
Data Compression and Modeling (3-0-3)
- Technical Interest
- Digital Signal Processing
- ECE 4270
- Catalog Description
- Theory and algorithms of signal encoding and decoding for data compression. Applications in information systems, digital telephony, digital television, and multimedia Internet.
- K. Sayood, Introduction to Data Compression (5th edition), Morgan Kaufmann, 2017. ISBN 9780128094747 (required)
A. Gersho, R. M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Press, 1992. ISBN 9780792391814(optional)
Gibson, Berger, Lookabaugh, Lindbergh and Baker, Digital Compression for Multimedia, Principles and Standards, Morgan Kaufmann, 1998. ISBN 9781558603691(optional)
SPIs are a subset of the abilities a student will be able to demonstrate upon successfully completing the course.
Outcome 1 (Students will demonstrate expertise in a subfield of study chosen from the fields of electrical engineering or computer engineering): 1. Understand the theory and practice of data compression and modeling as well as its applications in digital media and communication systems in everyday use. Outcome 2 (Students will demonstrate the ability to identify and formulate advanced problems and apply knowledge of mathematics and science to solve those problems): 1. Understand and implement methods and algorithms for data compression and modeling Outcome 3 (Students will demonstrate the ability to utilize current knowledge, technology, or techniques within their chosen subfield): 1. Design algorithms to model a given signal, extract model parameters, and achieve effective data compression on the signal
- Topical Outline
Introduction signal compression, lossless and lossy compression communication systems and building blocks: sources, channels, and codes issues - fixed rate and variable rate, robustness to channel errors, degradation and perceptual effects Quantization theory uniform quantization, distortion and bit rates amplitude distribution and high-rate quantization theory Bennett approximations and optimal performance, Lloyd's code optimality and algorithm elementary distortion-rate theory Architecture for data compression & introduction to data modeling signal models & spectral analysis quantization with memory fixed-rate vs. variable-rate code entropy, estimated entropy, complexity and typical sequence of an ergodic source variable rate quantization: lossless codes, prefix code Lossless Coding Techniques Huffman coding, arithmetic coding Universal lossless codes, adaptive and predictive lossless coding Distortion & Similarity Measures sample difference, sum of squared deviations and Euclidean distance Lp-norm, city-block distance, Mahalanobis distance transformation and transformation invariant similarity measures spectral distortion measures mutual-information, divergence, and Kullback-Liebler number perceptual issues Coding algorithms scalar quantization clustering algorithms for quantizer design the Lloyd algorithm and its generalization entropy-constrained quantizers Coding algorithms - vector quantization (VQ) sphere packing and optimal uniform lattice quantizers Lloyd algorithm - revisited progressive vector quantization variations of vector quantization finite-state VQ and Markov models tree and trellis encoding Applications speech and audio coding image and video coding Compression standards and formats Historical and evolutional aspects behind development of standards Application areas
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