Overview/Questions How do computers store text information? Why do - - PDF document

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John Magee

15 July 2013

CS101 Lecture 13: Text Representation and Data Compression

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Overview/Questions

– How do computers store text information? – Why do some characters show up as s on my browser? – What is compression, and why is it important? – How can data be compressed?

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Binary Representations

Recall: a single bit can be either a 0 or a 1 What if you need to represent more than 2 choices? n bits can represent 2n possible combinations

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Representing Text

There are finite number of characters to represent, so list them all and assign each a binary pattern. Character set A list of characters and the binary codes used to represent each one. Computer manufacturers agreed to standardize in the early 1960s.

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The ASCII Character Set

ASCII stands for American Standard Code for Information Interchange ASCII originally used seven bits to represent each character, allowing for 128 unique characters Later extended ASCII evolved so that all eight bits were used.

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The ASCII Character Set (7 bits)

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The Extended ASCII Character Set

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Can't You Take a Joke? :-)

Carnegie Mellon professor Scott E. Fahlman Proposed ASCII emoticons, Sept. 19, 1982. Source: http://www.wired.com/science/discoveries/news/2008/09/dayintech_0919

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ASCII Art

Text-based systems had no graphics… …so people created art and graphics out of ASCII text!

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The Unicode Character Set

Extended ASCII is not enough for international use. Unicode uses 16 bits per character How many characters can UNICODE represent? Unicode is a superset of ASCII. The first 256 characters correspond exactly to the extended ASCII character set

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The Unicode Character Set

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Recall: Morse Code

Invented by Samuel Morse for the telegraph in 1840s

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Text Compression

Problem: Assigning 16 bits to each character in a document uses a heck of a lot of space. We need ways to store and transmit text

• efficiently. Why?

Common compression techniques:

keyword encoding run-length encoding Huffman encoding

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Keyword Encoding

Replace frequently used words with a single character

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Keyword Encoding Example

Given the following paragraph,

We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness. That to secure these rights, Governments are instituted among Men, deriving their just powers from the consent of the governed, That whenever any Form of Government becomes destructive of these ends, it is the Right of the People to alter or to abolish it, and to institute new Government, laying its foundation on such principles and

• rganizing its powers in such form, as to them shall seem most

likely to effect their Safety and Happiness.

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Keyword Encoding Example

The encoded paragraph is

We hold # truths to be self-evident, $ all men are created equal, $ ~y are endowed by ~ir Creator with certain unalienable Rights, $ among # are Life, Liberty + ~ pursuit of Happiness. — $ to secure # rights, Governments are instituted among Men, deriving ~ir just powers from ~ consent of ~ governed, — $ whenever any Form of Government becomes destructive of # ends, it is ~ Right of ~ People to alter or to abolish it, + to institute new Government, laying its foundation on such principles + organizing its powers in such form, ^ to ~m shall seem most likely to effect ~ir Safety + Happiness.

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Keyword Encoding

Compression ratio The size of the compressed data divided by the size

• f the original data (0 < c.r. <= 1)

What did we save? Original paragraph: 656 characters Encoded paragraph: 596 characters Characters saved: 60 characters Compression ratio: 596/656 = 0.9085 Could we use this substitution chart for all text?

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Run-Length Encoding

Consider a single character which is repeated over and over again in a long sequence. Replace a repeated sequence with

– a flag character – repeated character – number of repetitions

Example: *n8

– * is the flag character – n is the repeated character – 8 is the number of times n is repeated

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Run-Length Encoding Example

Original text bbbbbbbbjjjkllqqqqqq+++++ Encoded text *b8jjjkll*q6*+5 (Why isn't l encoded? J?) The compression ratio is 15/25 or .6 Encoded text *x4*p4l*k7 Original text xxxxpppplkkkkkkk

This type of repetition isn’t very helpful for English text; can you think of a situation where it might be helpful?

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Huffman Encoding

Why should the character “X" and "z" take up the same number of bits as "e" or " "? Huffman codes use variable-length bit strings to represent each character. More frequently used letters have shorter strings to represent them.

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Huffman Encoding Example

ballboard would be

1010001001001010110001111011

compression ratio is 28/72 or 0.39 as compared to ASCII Try to encode roadbed

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Huffman Encoding

Prefix Property No character's bit string is the prefix of any

• ther character's bit string.

To decode

look for match left to right, bit by bit record letter when a match is found begin next character where you left off

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Huffman Encoding Example

Decode

1011111001010

Try it!

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Huffman Encoding

The technique for creating codes guarantees the prefix property of the codes. There is no single “Huffman code” -- each depends

• n the application. Two types of Huffman codes:

– general, based on use of letters in English, Spanish, …. – specialized, based on text itself or specific types of text

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Take-Away Points

– Character Sets, ASCII, Unicode – Data Compression – Key encoding – Run-length encoding – Huffman encoding

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Student To Dos

–Readings:

• Reed ch 5, pp 83-95

– HW03 (HTML) due Monday 11:59pm. – HW04 (Alice) due Thursday 11:59pm. – HW05 (Networking) to be posted.

Practice, Practice, Practice!