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Original. 10:1 Compression. 45:1 Compression. Compression. JPG compression, Source: http://www.dspguide.com/datacomp.htm. Content. Introduction Techniques for compression Run-length Lempel-Ziv Huffman Mpeg-4 Conclusion.

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compression

Original

10:1 Compression

45:1 Compression

Compression

JPG compression, Source: http://www.dspguide.com/datacomp.htm

content
Content
  • Introduction
  • Techniques for compression
    • Run-length
    • Lempel-Ziv
    • Huffman
  • Mpeg-4
  • Conclusion
slide4

Motivation for Compression

Compression is especially important in video, voice and fax

applications where very large amounts of data is transmitted.

Data compression can increase the throughput considerably.

Example

If there are 40,000 picture elements (pixels) per square inch.

on a 8.5" x 11" page, there are 3,740,000 bits.

Using a 56Kbps line, this transmission would take 67 seconds.

If the data is compressed by a factor of 10, the transmission time

is reduced to 6.7 seconds per page.

These days, data compression is commonly used by modems,

fax machines, video conferencing equipment, your TIVO, etc.

practical applications of data compression

Device 1

Bottleneck

Device 2

Practical applications of data compression
  • Realize cost savings in design of system:
  • Examples:
  • Modems, analog fax, compressed voice for cellular radio.
  • Digital voice
  • Compressed video, CD music, iPod
  • Without compression, these applications would not be feasible.
slide6

Principles behind Compression

  • Types of techniques:
  • 1. Redundancy reduction:
  • Remove redundancy from the message.
  • Usually lossless.
  • 2. Reduce information content:
  • Reduce the total amount of information in the message.
  • Leads to sacrifice of quality.
  • Usually lossy.
categories of compression
Categories of compression

1. Data compression

Used for data files and program files. Lossless.

e.g., Winzip, gzip, compress.

2. Audio compression.

Compresses digitized voice (e.g. cellular) and music.

Lossy for voice, lossless for hi-fi music. e.g. Real Audio.

3. Image compression

Removes redundancy within the frame. Different formats.

BMP (bitmap file) is lossless but creates large files.

GIF and JPEG lossy.

4. Video compression.

Removes intra- and inter-frame redundancy. Lossy.

Examples: MPEG, Quicktime, Real Video.

slide8

Compressibility of different data patterns

0 - CLOUDY DAY

1 - SUNNY DAY

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

SET 1:

0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0

SET 2:

0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0

SET 3:

0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0

SET 4:

0 1 0 1 1 1 0 0 0 1 1 0 0 1 0 1

In which set is the information content the highest?

How will you store these patterns of information in the most

economical way?

SET 5:

slide9

Compression Techniques

  • Common compression techniques
  • “Seinfeld” method: yada, yada, yada...
  • Run-length encoding
  • Lempel-Ziv method
  • Huffman coding

Marcy: Speaking of ex's, my old boyfriend came over late last night, and, yada yada yada, anyway. I'm really tired today.

spot the difference
Spot the difference…

That’s it. Image

Compressed 48 times

while you watched

slide11

RUN-LENGTH ENCODING

Source: NY Times, June 18, 1998.

slide12

RUN-LENGTH ENCODING

  • Look for sequences of repeating characters
  • Replace a sequence of repeating characters with a
  • 3-char code:
    • special character that indicates suppression
    • character to be suppressed
    • frequency (count of number of characters)
  • Example:
  • $******55.72 becomes $S*655.72
  • GunsbbbbbbbbbButter becomes GunsSb9Butter
  • What does the efficiency of this method depend on?
slide14

Lempel-Ziv Algorithm

This algorithm looks for repetitive sequences of patterns in a message and replaces them with a token which points back to the most recent occurrence.

The rain in spain falls mainly on the plain.

The rain [3,3]spain falls mainly on the plain.

Token [a,b] means: go back acharacters. copy b characters from there.

slide15

Lempel-Ziv Algorithm

This algorithm looks for repetitive sequences of patterns in a message and replaces them with a token which points back to the most recent occurrence.

The rain in spain falls mainly on the plain.

The rain [3,3]sp[9,4]falls mainly on the plain.

Token [a,b] means: go back acharacters. copy b characters from there.

slide16

Lempel-Ziv Algorithm

This algorithm looks for repetitive sequences of patterns in a message and replaces them with a token which points back to the most recent occurrence.

The rain in spain falls mainly on the plain.

The rain [3,3]sp[9,4]falls m[11,3]ly on the plain.

Token [a,b] means: go back acharacters. copy b characters from there.

slide17

Lempel-Ziv Algorithm

This algorithm looks for repetitive sequences of patterns in a message and replaces them with a token which points back to the most recent occurrence.

The rain in spain falls mainly on the plain.

The rain [3,3]sp[9,4]falls m[11,3]ly on [34,4]plain.

Token [a,b] means: go back acharacters. copy b characters from there.

slide18

Lempel-Ziv Algorithm

This algorithm looks for repetitive sequences of patterns in a message and replaces them with a token which points back to the most recent occurrence.

The rain in spain falls mainly on the plain.

The rain [3,3]sp[9,4]falls m[11,3]ly on [34,4]pl[15,3].

Token [a,b] means: go back acharacters. copy b characters from there.

This message contains 27 characters and 5 tokens.

Each token needs 2 bytes. Thus, space required is 37 bytes vs. original of 44 bytes.

(Note: Since each token takes two bytes,this replacement is done only if the repeating pattern is more than two bytes long. )

slide19

Huffman coding

Consider a language with only 4 characters, T, E, L, K.

Here is a pattern in this language:

T E E E L E E E K E

Probability of T = 0.1

Probability of E = 0.7

Probability of L = 0.1

Probability of K = 0.1

If we use 2-bit codes for each character, say,

00 - T; 01- E; 10- L; 11- K,

then we need 20 bits to store this pattern.

Question: Can we do better? i.e., store the pattern in fewer bits.

slide21

0

0.1

T

0.2

0

0.1

L

0.3

0

1

0.1

K

1.0

1

0.7

E

1

Codes:

T: 000

L: 001

K: 01

E: 1

HUFFMAN CODING EXAMPLE

  • Treat each character or symbol as leaf node in a tree (ordered by probability and occurrence)
  • Merge two lowest probability nodes into a node whose probability is the sum of the two merged nodes.
  • Repeat this process until no unmerged nodes remain. The final node is the root of a tree.
  • Label each pair of branches starting from root with 0 and 1
  • The code word for a symbol is the string of labels from the root node to the original symbol.
slide22

K

E

E

E

K

L

T

E

E

E

K

Codes:

T: 000

L: 001

K: 01

E: 1

Decoding a Message (start from left)

0 1 1 1 1 0 1 0 0 1 0 0 0 1 1 1 0 1

slide23

SAVINGS FROM HUFFMAN CODING

Original string had 10 characters, each 2 bits long.

Total length = 20 bits

Modified String:

T once -----> 1 x 3 = 3 bits

K once -----> 1 x 3 = 3 bits

L once -----> 1 x 2 = 2 bits

E 7 times -----> 7 x 1 = 7 bits

Total = 15 bits

Savings = (20-15) = 25 %

20

slide25

Applications and Standards

MNP Class 5 is a modem standard which uses run-length encoding.

V.42 bis is a newer modem standard for high-speed modems

These modems use Lempel-Ziv compression method and can compress by a factor of 3.5 to 4 times.

Video standards: H261, JPEG, MPEG-1 (for rates up to 1.5 Mbps), MPEG-2 (for rates up to 40 Mbps).

Audio compression standards: ADPCM, LPC (Linear Predictive Coding), MPEG Audio (e.g., MP3)

In general, compression ratio depends upon nature of data

mpeg 4
MPEG-4
  • The “bane” of DVD?
  • A standard for transmitting video and sound
  • Meshes existing MPEG-2 inter- and intra-frame advancements with VRML
  • What about MPEG-7?
slide30

Conclusion

Anything can be compressed more…

…but can the original form be recreated?

Big Bang: The ultimate decompression!

Image source: http://www.esa.int/esaKIDSen/SEMSZ5WJD1E_OurUniverse_0.html