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Instructor Class Description

Time Schedule:

Michael D Stiber
CSS 457
Bothell Campus

Multimedia and Signal Computing

How multimedia information is captured, represented, processed, communicated, and stored in computers. Topics include: physical properties of sound and images, digitization, digital signal processing, filtering, compression, JPEG and MPEG algorithms, and storage and network communication. Prerequisite: either CSS 263 or CSS 342; may not be repeated.

Class description

One of the fastest growing application areas for computers is the processing of multimedia -- sound, images, and video. Multimedia places great demands on processing power, network bandwidth, storage capacity, I/O speed, and software design. In this course, you will learn how multimedia information is captured, represented, processed, communicated, and stored in computers. The specific topics we will cover include: physical properties of multimedia source information (sound, images), devices for information capture (microphones, cameras), digitization, compression, digital media representation (JPEG, MPEG), digital signal processing, and network communication. By the end of this course, you should understand the problems and solutions facing multi/hypermedia systems development in the areas of user interfaces, information retrieval, data structures and algorithms, and communications. As a result, you should be well-prepared to work with electrical engineers in the design of advanced signal processing systems (e.g., wireless communication devices) and multimedia computing systems.

Student learning goals

What signals are like in the "real" world and how the properties of multimedia signals (sounds, images, video) affect how we perceive them.

How to use mathematics as a tool to make problem solving simpler, for example, converting laborious trigonometric computations to straightforward algebra with polynomials.

How these signals get into the computer, how they are represented within the computer, and the tradeoffs among sampling speed, levels of quantization, and file size.

What are the basic algorithms that perform simple signal processing to remove noise, emphasize important features, etc. You should be well-prepared to work with electrical engineers in the design of more advanced signal processing systems.

How multimedia file sizes can be reduced by compression, and the tradeoffs among compression, processing overhead, and media quality.

How these concepts are applied in multimedia applications and standards.

General method of instruction

Recommended preparation

This course covers much of the mathematical foundations for understanding signals and signal processing, however, it is assumed that you are familiar with topics such as complex numbers, trigonometry, derivatives, vectors, the basic idea of integrals, infinite series, and basic physics (mass, acceleration, force). CSS 342 and lower division math courses are the only formal prerequisites. While we may do some programming, this is not a programming course.

Class assignments and grading

We will be using J-DSP for the bulk of our computing laboratories. J-DSP is a Java applet that lets you build signal processing systems by assembling block diagrams.

The information above is intended to be helpful in choosing courses. Because the instructor may further develop his/her plans for this course, its characteristics are subject to change without notice. In most cases, the official course syllabus will be distributed on the first day of class.
Course web page
Last Update by Michael D Stiber
Date: 03/22/2007