This is supplemental course information, designed to give you a fuller picture of the course and an expanded look at the topics covered. This is an unofficial document. The USC Course Catalog is the binding description of all university courses. Information such as books, materials covered, and the order of topics is subject to change. Please consult instructor for this semseter to get more upto date course information.
Catalog Data:
469 Introduction to Digital Media Engineering (3) Fundamentals of digital media representation, for audio, images and video signals. Sampling; Fourier and z-transforms; FFT; filter design; image segmentation, image and video compression standards. Prerequisite: EE 301a or EE 321; EE 364 or MATH 407.
Text book:
Introduction to Data Compression, 2nd Ed., K. Sayood, Morgan Kaufman Publishers, 2001
Course Coordinators:
Antonio Ortega, Associate Professor of Electrical Engineering
Topics:
1. Introduction to the fundamentals of signal representation. Sampling of continuous signals. Signal spaces and bases. Inner product and orthogonal bases.
2. Signal transforms used for 1D/2D digital media. Discrete Fourier Transform and Discrete Cosine Transform. Wavelet transform and multirate filterbanks.
3. Quantization design. Optimal quantizers. Vector quantizers. Practical quantizers used in widely used compression schemes.
4. Fundamentals of Entropy Coding. Concept of entropy. Practical entropy coding techniques.
5. Overview of international standards for the compression of speech, audio, images and video, with a particular emphasis on JPEG, MP3, and MPEG-2.
Course Objectives:
To provide students with an overview of the key fundamental concepts that underline widely used technology for digital media compression. The emphasis is in teaching the fundamental underlying ideas (signal representation with basis, quantization, entropy coding, etc.) rather than the specifics of the technology currently in use.
Course Outcomes:
The students will be able to:
1. Understand basic principle in signal representation, and why signal transforms (e.g., DCT or Wavelets) are popular tools to represent signals for compression.
2. Experiment with those transforms on real signals, including audio and images, and be able to verify how lossy compression can be made more efficient through the use of perceptual masking.
3. Understand the basic theory of lossless compression, including the concept of entropy.
4. Design and test simple entropy coders for real signals, including for example the use of Huffman coding design.
5. Understand the basic concepts of quantization, including optimal quantization.
6. Design and test a variety of quantizers to be used on real signals.
7. Understand the basic ideas behind motion estimation for video compression.
8. Have an overall knowledge of several key technologies used in everyday life (e.g., MP3, JPEG, MPEG-2) and most importantly to know why they work.
9. Understand how basic general concepts are used in the design of broadly adopted international standards; trade-offs between performance and complexity.
Laboratory Projects:
5 homeworks including both theory and Matlab based exercises. Some of these exercises can include more open ended questions which may have more than one possible solution.
Prepared by: Antonio Ortega Date: September 16, 2002 
