IT-101

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IT-101
Introduction to Information Technology

• Lecture 4

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• Overview

• Chapter 7
• Compressing Information
• Why can information be compressed
• Variable length coding
• Universal coding
• Chapter 82
• Image Compression
• Lossless compression
• Lossy compression
• Chapter 9
• Digital Video
• MPEG video compression

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Compressing Information

• Compression techniques can significantly reduce
  the bandwidth and memory required for sending,
  receiving, and storing data.
• Most computers are equipped with modems that
  compress or decompress all information leaving or
  entering via the phone line.
• With a mutually recognized system (e.g. WinZip)
  the amount of data can be significantly
  diminished.
• Examples of compression techniques
• Compressing BINARY DATA STREAMS
• Variable length coding (e.g. Huffman coding)
• Universal Coding (e.g. WinZip)
• IMAGE-SPECIFIC COMPRESSION
• GIF and JPEG
• VIDEO COMPRESSION
• MPEG

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Why can we compress information?

• When large information files are moved around the
  internet, they are often compressed prior to
  transmission then decompressed after delivery
  to reduce the time and resources needed for
  delivery.
• For example, two still images from a video
  sequence of images are often similar. This fact
  can be exploited by transmitting only the changes
  from one image to the next.

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• Some characters/events occur more frequently than
  others.
• Its possible to represent frequently occurring
  characters with a smaller number of bits during
  transmission.
• This may be accomplished by a variable length
  code, as opposed to a fixed length code like
  ASCII.
• An example of a simple variable length code is
  Morse Code.
• E occurs more frequently than Z so we
  represent E with a shorter length code

. E - T - - . . Z -
- . - Q
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Information Theory

• Variable length coding exploits the fact that
  some information occurs more frequently than
  others.
• The mathematical theory behind this concept is
  known as INFORMATION THEORY
• Claude E. Shannon developed modern Information
  Theory at Bell Labs in 1948.
• He saw the relationship between the probability
  of appearance of a transmitted signal and its
  information content.
• This realization enabled the development of
  techniques that could achieve compression.

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A Little Probability

• Shannon (and others) found that information can
  be related to probability.
• An event has a probability of 1 (or 100) if we
  believe this event will occur.
• An event has a probability of 0 (or 0) if we
  believe this event will not occur.
• The probability that an event will occur takes on
  values anywhere from 0 to 1.
• Consider a coin toss heads or tails each has a
  probability of .50
• In two tosses, the probability of tossing two
  heads is
• 1/2 x 1/2 1/4 or .25
• In three tosses, the probability of tossing all
  tails is
• 1/2 x 1/2 x 1/2 1/8 or .125
• We compute probability this way because the
  result of each toss is independent of the
  results of other tosses.

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Variable Length Coding

• Unlike fixed length codes like ASCII, variable
  length codes
• Assign the longest codes to the most infrequent
  events.
• Assign the shortest codes to the most frequent
  events.
• Each code must be uniquely identifiable
  regardless of length.
• Examples of Variable Length Coding
• Morse Code
• Huffman Coding

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Morse Code

• Characters represented by patterns of dots and
  dashes.
• More frequently used letters use short code
  symbols.
• Short pauses are used to separate the letters.
• Represent Hello using Morse Code
• H . . . .
• E .
• L . - . .
• L . - . .
• O - - -
• Hello . . . . . . - . . . - . . - - -

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Huffman Coding
A minimal variable-length character coding based
on the frequency of each character

• Huffman coding is a variable length coding scheme
• It assigns the shortest codes to the most
  frequently occurring events, and longest codes to
  the least frequent events

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Universal Coding

• Universal Coding schemes do not require a
  knowledge of the statistics (probabilities) of
  the events to be coded.
• Universal Coding is based on the realization that
  any stream of data consists of some repetition.
• Lempel-Ziv coding is one form of Universal Coding
  presented in the text.
• Compression results from reusing frequently
  occurring strings.
• Works better for long data streams. Inefficient
  for short strings.
• Used by WinZip to compress information.

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Hue Luminance Saturation

• One approach to color representation of images is
  Hue, Luminance and Saturation (HLS)
• This system does not represent colors by
  combinations of other colors, but it still uses 3
  numerical values
• Hue Represents where the pure color component
  falls on a scale that extends across the visible
  light spectrum
• Luminance Represents how light or dark a pixel
  is
• Saturation How pure the color is, i.e. how
  much it is diluted by the addition of white (100
  saturation means no dilution with white)

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Video

• Human perception of movement is slow
• Studies show that humans can only take in 20
  different images (frames) per second before they
  begin to blur together
• If these images are sufficiently similar, then
  the blurring which takes place appears to the eye
  to resemble motion, in the same way we discern it
  when an object moves smoothly in the real world.
• We can detect higher rates of flicker, but only
  to about 50 per second
• This phenomenon has been used since the beginning
  of the 20th century to produce moving pictures''
  or movies.
• Movies show 24 frames (images) per second, but
  flash at 48 per second

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Image Compression

• Near Photographic Quality Image
• 1,280x800 pixels, with 24 bits of color
  information per pixel
• Total 1280x800x2424,576,000 bits
• 56 Kbps modem
• 56,000 bits/sec
• How long does it take to download?
  24,576,000/56,000 439 seconds/60 7.31
  minutes

Obviously image compression is essential.
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Image Compression

• 2 types of image compression
• Lossless image compression - In lossless image
  compression, all the original data is restored
  upon decompression, but the amount of
  compression which can be achieved is not very
  high.
• Lossy image compression - In lossy image
  compression, some of the original data is lost,
  but a high degree of image compression can be
  achieved.
• Generally, the higher the degree of compression,
  the higher the data loss.
• A high degree of image compression is essential
  to reduce both download time and storage space

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Lossless Image Compression

• Every detail of original data is restored upon
  decompression
• Examples Run Length Encoding, JPEG, GIF
• Run Length Encoding
• Used in an image format called PCX, commonly used
  in PCs
• Provides a small amount of compression
• Idea behind it is that if a long string of pixels
  are identical, then compression can be achieved
  by only sending a special code that represents
  that string of pixels
• e.g Instead of sending 111111111111111111111111,
  we can send the same information as 24 1s

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Lossless Image Compression

• JPEG
• Short for Joint Photographic Experts Group
• One of the most commonly used image compression
  technique/standard
• Can provide high compression
• 29 distinct coding systems for compression, of
  which 2 are for Lossless compression
• Lossless JPEG uses a technique called predictive
  coding to attempt to identify pixels later in the
  image in terms of previous pixels in that same
  image

The two compressed image formats most often
encountered on the Web are JPEG and GIF.
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Lossy Image Compression

• GIF
• Short for Graphics Interchange Format
• A commonly encountered compressed image format
• Provides a moderate amount of compression
• Developed by CompuServe, one of the first
  companies to provide telephone based computer
  network access
• Uses the Lempel-Ziv-Welch algorithm to compress
  data

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Digital Video Compression (MPEG)
Motion Picture Expert Group (MPEG) standard for
video compression.

• MPEG is a series of techniques for compressing
  streaming digital information
• MPEG Standards
• Mpeg- 1 intended for the compression of video for
  storage on CD media. Most common used video
  format
• Mpeg -2 Most commonly used for DVD- Video
• Mpeg 4 Video Teleconferencing which sacrifices
  the video quality for the need to obtain very low
  bit rates , supported by phone lines

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Comments for next class

• Go over todays lecture notes
• Review for Mid Term next class
• Once you complete your exam, you will receive the
  take home quiz Due 3/24/05

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Additional Comments

• Class is Canceled on 3/10/05
• Spring Break 3/17/05
• Homework 4 will consist of you creating a Virtual
  World. This assignment is due March 24th, 2005.
• First download Alice (http//www.alice.org/) or
  any VR freeware you may find on
  http//vresources.jump-gate.com/.
• Select software from the left-hand menu
• Scroll down until you reach Public domain,
  academic and freeware VR world creation tools
• Download Alice.
• Once you download Alice
• Extract the files and save the file to a folder
  on your desktop
• Double click on the icon Alice
• Begin the tutorial
• After competition of the tutorial add 15 new
  features to your Virtual World.
• Save as a HTML document and email to me by March
  24th 600 PM.