CS计算机代考程序代写 Java data structure Excel interpreter algorithm CS61B

CS61B
Lectures 38: Compression
● Prefix Free Codes
● Huffman Coding
● Theory of Compression
● LZW (Extra)
● Lossy Compression (Extra)

Zip Files, How Do They Work?
$ zip mobydick.zip mobydick.txt
adding: mobydick.txt (deflated 59%)
$ ls -l
-rw-rw-r– 1 jug jug 643207 Apr 24 10:55 mobydick.txt
-rw-rw-r– 1 jug jug 261375 Apr 24 10:55 mobydick.zip
File is unchanged by zipping / unzipping.
Size in Bytes
$ unzip mobydick.zip
replace mobydick.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: r
new name: unzipped.txt
inflating: unzipped.txt
$ diff mobydick.txt unzipped.txt
$

Compression Model #1: Algorithms Operating on Bits
Bitstream B
01010101000001010101110…
Compressed bits C(B)
1001010101…
Compression Algorithm C
C(B)
1001010101…
B
Decompression Algorithm C-1
01010101000001010101110…
In a lossless algorithm we require that no information is lost. ● Text files often compressible by 70% or more.

Prefix Free Codes

Increasing Optimality of Coding
By default, English text is usually represented by sequences of characters, each 8 bits long, e.g. ‘d’ is 01100100.
word
binary
hexadecimal
dog
01100100 01101111 01100111
64 6f 67
Easy way to compress: Use fewer than 8 bits for each letter.
● Have to decide which bit sequences should go with which letters.
● More generally, we’d say which codewords go with which symbols.

More Code: Mapping Alphanumeric Symbols to Codewords
Example: Morse code.
● Goal: Compact representation.
● What is – – • – – •?

More Code: Mapping Alphanumeric Symbols to Codewords
Example: Morse code.
● Goal: Compact representation.
● What is – – • – – •? It’s ambiguous!
○ MEME ○ GG
Note:
● Can think of dot as 0, dash as 1.
● Operators pause between codewords
to avoid ambiguity.
○ Pause acts as a 3rd symbol.
Alternate strategy: Avoid ambiguity by making code prefix free.

Morse Code (as a Tree)
From Wikimedia

Prefix-Free Codes [Example 1]
A prefix-free code is one in which no codeword is a prefix of any other. Example for
English:
0
start
1
0 01
1
space
E
space
1
E
01
T
001
A
0001
O
00001
I
000001

01
T
A
O
I
01
01
01
I ATE: 0000011000100101

Prefix-Free Codes [Example 2]
A prefix-free code is one in which no codeword is a prefix of any other. Example for English:
0 start 1 01
space
111
E
010
T
1101
A
1011
O
1001
I
1000

01

0
101
1
0
0
1
space
E …
010101
I O … A … T
I ATE: 100011110111101010

Prefix Free Code Design
Observation: Some prefix-free codes are better for some texts than others.
space
1
E
01
T
001
A
0001
O
00001
I
000001

space
111
E
010
T
1101
A
1011
O
1001
I
1000

Better for EEEEAT (8+3+4 = 15 bits).
Worse for EEEEAT (12+4+4 = 20 bits).
Much worse for JOSH (25+5+8+10 = 48 bits).
Better for JOSH (7+4+6+6 = 23 bits).
Observation: It’d be useful to have a procedure that calculates the “best” code for a given text.

Shannon Fano Codes (Extra)

Code Calculation Approach #1 (Shannon-Fano Coding)
● Count relative frequencies of all characters in a text.
● Split into ‘left’ and ‘right halves’ of roughly equal frequency.
○ Left half gets a leading zero. Right half gets a leading one. ○ Repeat.
Symbol
Frequency

0.35

0.17

0.17

0.16

0.15
Left half
35% of all characters are 我 我爸是李刚
Right half

Code Calculation Approach #1 (Shannon-Fano Coding)
● Count relative frequencies of all characters in a text.
● Split into ‘left’ and ‘right halves’ of roughly equal frequency.
○ Left half gets a leading zero. Right half gets a leading one. ○ Repeat.
Symbol
Frequency
Code

0.35
0…

0.17
0…

0.17
1…

0.16
1…

0.15
1…
Left half
01


是李

Right half

Code Calculation Approach #1 (Shannon-Fano Coding)
● Count relative frequencies of all characters in a text.
● Split into ‘left’ and ‘right halves’ of roughly equal frequency.
○ Left half gets a leading zero. Right half gets a leading one. ○ Repeat.
Symbol
Frequency
Code

0.35
00

0.17
01

0.17
1…

0.16
1…

0.15
1…
Left half Right half
01
01
我爸
是李

Code Calculation Approach #1 (Shannon-Fano Coding)
● Count relative frequencies of all characters in a text.
● Split into ‘left’ and ‘right halves’ of roughly equal frequency.
○ Left half gets a leading zero. Right half gets a leading one. ○ Repeat.
Symbol
Frequency
Code

0.35
00

0.17
01

0.17
1…

0.16
1…

0.15
1…
Left half
Right half
01
01
我爸
是李

Code Calculation Approach #1 (Shannon-Fano Coding)
● Count relative frequencies of all characters in a text.
● Split into ‘left’ and ‘right halves’ of roughly equal frequency.
○ Left half gets a leading zero. Right half gets a leading one. ○ Repeat.
Symbol
Frequency
Code

0.35
00

0.17
01

0.17
10

0.16
11…

0.15
11…
Left half
Right half
01
0101
我爸是

Code Calculation Approach #1 (Shannon-Fano Coding)
● Count relative frequencies of all characters in a text.
● Split into ‘left’ and ‘right halves’ of roughly equal frequency.
○ Left half gets a leading zero. Right half gets a leading one. ○ Repeat.
Symbol
Frequency
Code

0.35
00

0.17
01

0.17
10

0.16
110

0.15
111
01
0101
我爸是
01
李刚

Code Calculation Approach #1 (Shannon-Fano Coding)
Shannon-Fano coding is NOT optimal. Does a good job, but possible to find ‘better’ codes (see CS170).
● Optimal solution assigned (and solved) as alternative to a final exam: http://www.huffmancoding.com/my-uncle/scientific-american
01
0101
Symbol
Frequency
Code

0.35
00

0.17
01

0.17
10

0.16
110

0.15
111
我爸是
01
李刚

Huffman Coding

Code Calculation Approach #2: Huffman Coding
Calculate relative frequencies.
● ●

Assign each symbol to a node with weight = relative frequency.
Take the two smallest nodes and merge them into a super node with weight equal to sum of weights.
Repeat until everything is part of a tree.
35% of characters in
input are 我. 16% of characters in
input are 李.
0.31 0.34 0.31
01 0101
我爸是李刚 我爸是李刚 我爸是李刚 0.35 0.17 0.17 0.16 0.15 0.35 0.17 0.17 0.35

Code Calculation Approach #2: Huffman Coding
Calculate relative frequencies.
● ●

Assign each symbol to a node with weight = relative frequency.
Take the two smallest nodes and merge them into a super node with weight equal to sum of weights.
Repeat until everything is part of a tree.
1 01 001
0.34 0.31
0101 0101 0101
0.65
我爸是李刚 我爸是李刚 我爸是李刚 0.35 0.35

Efficiency Assessment: http://yellkey.com/start
How many bits per symbol do we need to compress a file with the character
frequencies listed below using the Huffman code that we created?
A. (1*1 + 4*3) / 5
= 2.6 bits per symbol
B. (0.35) * 1 + (0.17 + 0.17 + 0.16 + 0.15) * 3 = 2.3 bits per symbol
C. Not enough information, we need to know the exact characters in the file being compressed.
Symbol
Frequency
Huffman Code

0.35
0

0.17
100

0.17
101

0.16
110

0.15
111

Efficiency Assessment of Huffman Coding
How many bits per symbol do we need to compress a file with the character frequencies listed below using the Huffman code that we created?
B. (0.35) * 1 + (0.17 + 0.17 + 0.16 + 0.15) * 3
= 2.3 bits per symbol.
Example assuming we have 100 symbols:
● 35 * 1 = 35 bits
● 17 * 3 = 51 bits
● 17 * 3 = 51 bits
● 16 * 3 = 48 bits
● 15 * 3 = 45 bits
Total: 230 bits 230 / 100 = 2.3 bits/symbol
Symbol
Frequency
Huffman Code

0.35
0

0.17
100

0.17
101

0.16
110

0.15
111

Efficiency Assessment of Huffman Coding
If we had a file with 350 我 characters , 170 爸 characters , 170 是 characters, 160 李 characters, and 150 刚 characters, how many total bits would we need to encode this file using 32 bit Unicode? Using our Huffman code?
You don’t need a calculator.
Symbol
Frequency
Huffman Code

0.35
0

0.17
100

0.17
101

0.16
110

0.15
111
2.30 bits per symbol for texts with this distribution

Efficiency Assessment of Huffman Coding
If we had a file with 350 我 characters , 170 爸 characters , 170 是 characters, 160 李 characters, and 150 刚 characters, how many total bits would we need to encode this file using 32 bit Unicode? Using our Huffman code?
1000 total characters. Space used:
● 32 bit Unicode: 32,000 bits.
● Huffman code: 2,300 bits.
Our code is 14 times as efficient!
● Can only encode strings with these 5 symbols.
2.30 bits per symbol for texts with this distribution
Symbol
Frequency
Huffman Code

0.35
0

0.17
100

0.17
101

0.16
110

0.15
111

Huffman vs. Shannon-Fano
Shannon-Fano code below results in an average of 2.31 bits per symbol, whereas Huffman is only 2.3 bits per symbol.
● Huffman coded file is 0.35*1 + 0.65*3 = 2.3 bits per symbol.
Symbol
Frequency
S-F Code
Huffman Code

0.35
00
0

0.17
01
100

0.17
10
101

0.16
110
110

0.15
111
111
Strictly better than Shannon-Fano coding. There is NO downside to Huffman coding instead.

Huffman Coding Data Structures

Prefix-Free Codes
Question: For encoding (bitstream to compressed bitstream), what is a natural data structure to use? Assume characters are of type Character, and bit sequences are of type BitSequence.
space
1
E
01
T
001
A
0001
O
00001
I
000001

space
111
E
010
T
1101
A
1011
O
1001
I
1000

0111
I ATE: 0000011000100101 I ATE: 100011110111101010

Prefix-Free Codes
Question: For encoding (bitstream to compressed bitstream), what is a natural data structure to use? chars are just integers, e.g. ‘A’ = 65. Two approaches:
● Array of BitSequence[], to retrieve, can use character as index.
● How is this different from a HashMap? Lookup in
a hashmap consists of:
○ Compute hashCode.
○ Mod by number of buckets.
○ Look in a linked list.
Compared to HashMaps, Arrays are faster (just get the item from the array), but use more memory if some characters in the alphabet are unused.
I ATE: 0000011000100101 I ATE: 100011110111101010

Prefix-Free Codes
Question: For decoding (compressed bitstream back to bitstream), what is a natural data structure to use?
space
1
E
01
T
001
A
0001
O
00001
I
000001

space
111
E
010
T
1101
A
1011
O
1001
I
1000

0111
I ATE: 0000011000100101 I ATE: 100011110111101010

Prefix-Free Codes
Question: For decoding (compressed bitstream back to bitstream), what is a natural data structure to use?
● We need to look up longest matching prefix, an operation that Tries excel at.
space
1
E
01
T
001
A
0001
O
00001
I
000001

space
111
E
010
T
1101
A
1011
O
1001
I
1000

0111
I ATE: 0000011000100101 I ATE: 100011110111101010

Prefix-Free Codes
Question: For decoding (compressed bitstream back to bitstream), what is a natural data structure to use?
● We need to look up longest matching prefix, an operation that Tries excel at.
space
1
E
01
T
001
A
0001
O
00001
I
000001

space
111
E
010
T
1101
A
1011
O
1001
I
1000

0111
I ATE: 0000011000100101 I ATE: 100011110111101010

Prefix-Free Codes
Question: For decoding (compressed bitstream back to bitstream), what is a natural data structure to use?
● We need to look up longest matching prefix, an operation that Tries excel at.
space
1
E
01
T
001
A
0001
O
00001
I
000001

space
111
E
010
T
1101
A
1011
O
1001
I
1000

0111
I ATE: 0000011000100101 I ATE: 100011110111101010

Prefix-Free Codes
Question: For decoding (compressed bitstream back to bitstream), what is a natural data structure to use?
● We need to look up longest matching prefix, an operation that Tries excel at.
space
1
E
01
T
001
A
0001
O
00001
I
000001

space
111
E
010
T
1101
A
1011
O
1001
I
1000

0111
I ATE: 0000011000100101 I ATE: 100011110111101010

Huffman Coding in Practice

Huffman Compression
Two possible philosophies for using Huffman Compression:
1. For each input type (English text, Chinese text, images, Java source code, etc.), assemble huge numbers of sample inputs for that category. Use each corpus to create a standard code for English, Chinese, etc.
2. For every possible input file, create a unique code just for that file. Send the code along with the compressed file.
What are some advantages/disadvantages of each idea? Which is better?
$ java HuffmanEncodePh1 ENGLISH mobydick.txt
$ java HuffmanEncodePh1 BITMAP horses.bmp
$ java HuffmanEncodePh2 mobydick.txt
$ java HuffmanEncodePh2 horses.bmp

Huffman Compression (Your Answers)
Two possible philosophies for using Huffman Compression:
1. Build one corpus per input type.
2. For every possible input file, create a unique code just for that file. Send the
code along with the compressed file.
What are some advantages/disadvantages of each idea? Which is better?
● First one: What if the file has multiple data types, e.g. Chinese and English.
● Second one: Individual code is more “secure”.
● Second one: Compression for each file requires more work.
● Third approach: Try every code, and use the best one.
● First approach: Could be faster, because you have already built a code.
● If data is not compressible, may as well use #1.
● Both allow you to support arbitrary file types.

Huffman Compression (My Answers)
Two possible philosophies for using Huffman Compression:
1. Build one corpus per input type.
2. For every possible input file, create a unique code just for that file. Send the
code along with the compressed file.
What are some advantages/disadvantages of each idea? Which is better?
● Approach 1 will result in suboptimal encoding.
● Approach 2 requires you to use extra space for the codeword table in the
compressed bitstream.
For very large inputs, the cost of including the codeword table will become insignificant.

Huffman Compression
Two possible philosophies for using Huffman Compression:
1. For each input type (English text, Chinese text, images, Java source code, etc.), assemble huge numbers of sample inputs for that category. Use each corpus to create a standard code for English, Chinese, etc.
2. For every possible input file, create a unique code just for that file. Send the code along with the compressed file.
In practice, Philosophy 2 is used in the real world.

Huffman Compression Example [Demo Link]
Given input text: 我我刚刚刚是我是我刚李刚我李是爸李爸李是李我我李刚 是我是刚爸是刚我爸我李是是李是我我刚爸是李我我我是爸我是我爸是我 爸是我是刚我是爸刚爸我刚我我刚爸我我爸我刚爸爸李李李李我我爸李我 我刚爸李我我李我爸我我
Step 1: Count frequencies.
Step 2: Build encoding array and decoding trie.
Step 3: Write decoding trie to output.huf.
Step 4: Write codeword for each symbol to output.huf.
Output bits: 010101010101001…00111111111101… Decoding Trie Codewords
1
01
0101
我爸是李刚 0.35 0.17 0.17 0.16 0.15
Decoding Trie
See optional textbook for how to do this.
0

Huffman Decompression Example [Demo Link]
Given input bitstream: 010101010101001…00111111111101…
Decoding Trie Codewords
Step 1: Read in decoding trie.
Step 2: Use codeword bits to walk down the trie, outputting symbols every time you reach a leaf.
● Note: Symbols are really just bits!
○ 我 is 111001011000100010010001 in Unicode.
○ “Outputting 我” actually means outputting these 32 bits.
1
01
0101
我爸是李刚 0.35 0.17 0.17 0.16 0.15
0
Output symbols: 我我刚刚刚是…
● Output bits: 111001011000100010010001…

Huffman Coding Summary
Given a file X.txt that we’d like to compress into X.huf:
● Consider each b-bit symbol (e.g. 8-bit chunks, Unicode characters, etc.) of X.txt, counting occurrences of each of the 2b possibilities, where b is the size of each symbol in bits.
● Use Huffman code construction algorithm to create a decoding trie and encoding map. Store this trie at the beginning of X.huf.
● Use encoding map to write codeword for each symbol of input into X.huf. To decompress X.huf:
● Read in the decoding trie.
● Repeatedly use the decoding trie’s longestPrefixOf operation until all bits in
X.hug have been converted back to their uncompressed form.
See Huffman.java for an example implementation on 8-bit symbols.

Compression Theory

Compression Algorithms (General)
The big idea in Huffman Coding is representing common symbols with small numbers of bits.
Many other approaches, e.g.
● Run-length encoding: Replace each character by itself concatenated with the number of occurrences.
○ Rough idea: XXXXXXXXXYYYYXXXXX -> X10Y4X5
● LZW: Search for common repeated patterns in the input. See extra slides.
General idea: Exploit redundancy and existing order inside the sequence.
● Sequences with no existing redundancy or order may actually get enlarged.

Comparing Compression Algorithms
Different compression algorithms achieve different compression ratios on different files.
We’d like to try to compare them in some nice way.
● To do this, we’ll need to refine our model from slide 3 to be a bit more sophisticated.
Let’s start with a straightforward puzzle.

SuperZip
Suppose an algorithm designer says their algorithm SuperZip can compress any bitstream by 50%. Why is this impossible?

Universal Compression: An Impossible Idea
Argument 1: If true, they’d be able to compress any bitstream down to a single bit. Interpreter would have to be able to do the following (impossible) task for ANY output sequence.
01010101000001010101110
Compression
1 Compression
101010100001
111001
Compression
Compression
Compression
00
101
1 00 101 111001
101010100001 01010101000001010101110

Universal Compression: An Impossible Idea
Argument 2: There are far fewer short bitstreams than long ones. Guaranteeing compression even once by 50% is impossible. Proof:
● There are 21000 1000-bit sequences.
● There are only 1+2+4+…+2500 = 2501 – 1 bit streams of length ≤ 500.
● In other words, you have 21000 things and only 2501 – 1 places to put them.
● Of our 1000-bit inputs, only roughly 1 in 2499 can be compressed by 50%!

A Sneaky Situation
Universal compression is impossible, but the following example implies that comparing compression algorithms could still be quite difficult.
Suppose we write a special purpose compression algorithm that simply hardcodes small bit sequences into large ones.
● Example, represent GameOfThronesSeason6-Razor1911-Rip-Episode1.mp4 as 010.
Compressed Bits
010
3 bits
8927472560 bits
00000000001111111111…
GameOfThronesSeason6- Razor1911-Rip-Episode1. mp4
Decompression Algorithm C-1

A Sneaky Situation
Suppose we write a special purpose compression algorithm that simply hardcodes small bit sequences into large ones.
● Example, represent GameOfThronesSeason6-Razor1911-Rip-Episode1.mp4 as 010.
To avoid this sort of trickery, we should include the bits needed to encode the decompression algorithm itself.
Compressed Bits
010
3 bits
8927472560 bits
00000000001111111111…
GameOfThronesSeason6- Razor1911-Rip-Episode1. mp4
Decompression Algorithm C-1
8927472707 bits

Compression Model #2: Self-Extracting Bits
As a model for the decompression process, let’s treat the algorithm and the compressed bitstream as a single sequence of bits.
● If you want a concrete idea to hold on to, imagine storing the compressed bitstream as a byte[] variable in a .java file. We’ll show an example on the coming slides involving compressing an image.
● Can think of the algorithm + compressed bitstream as an input to an interpreter. Interpreter somehow executes those bits (see 61A)
○ At the very “bottom” of these abstractions is some kind of physical
machine (see 61C).
GameOfThronesSeason6-R azor1911-Rip-Episode1.mp4
0100001001001101…
8,927,472,560 bits
0111000001110101…
SelfExtractingGoT.java
Interpreter
8,927,472,707 bits

HugPlant
Huffman Coding can be used to compress any data, not just text. In bitmap format, the plant below is simply the stream of bits shown on the right.
Original Uncompressed Bits B
42 4d 7a 00 10 00 00 00 00
00 7a 00 00 00 6c 00 00 00
00 02 00 00 00 02 00 00 01
00 20 00 03 00 00 00 00 00
10 00 12 0b 00 00 12 0b 00
00 00 00 00 00 00 00 00 00
00 00 ff 00 00 ff 00 00 ff
00 00 00 00 00 00 ff 01 00
00 00 00 00 00 00 00 00 00
00 01 00 00 00 00 00 00 00
00 00 00 00 01 00 …
Total: 8389584 bits

HugPlant Compressed
42 4d 7a 00 10 00 00 00 00 00 7a 00 00 00 6c 00 00 00 00 02 00 00 00 02 00 00 01 00 20 00 03 00 00 00 00 00 10 00 12 0b 00 00 12 0b 00 00 00 00 00 00 00 00 00 00 00 00 ff 00 00 ff 00 00 ff 00 00 00 00 00 00 ff 01 00 00 00 00 00 00 00 00 00 00 00 01 00 00 00 00 00 00 00 00 00 00 00 01 00 … Total: 8389584 bits
Huffman.java
compress()
74 68 65 20 70 61 73 73 63 6F 64 65 20 69 73 20
68 75 67 39 31 38 32 37 78 79 7A 2E 65 75 7a c0
09 eb cd d4 2a 55 9f d8 98 d1 4e e7 97 56 58 68
0c 7a 43 dd 80 00 7b 11 58 f4 75 73 77 bc 26 01
e0 92 28 ef 47 24 66 9b de 8b 25 04 1f 0e 87 bd
87 9e 03 c9 f1 cf ad fa 82 dc 9f a1 31 b5 79 13
9b 95 d5 63 26 8b 90 5e d5 b0 17 fb e9 c0 e6 53
c7 cb dd 5f 77 d3 bd 80 f9 b6 5e 94 aa 74 34 3a
a9 c1 ca e6 b8 9c 60 ab 36 3b a5 8a b4 3a 5c 5a
62 e9 2f 16 4c 34 60 6e 51 28 36 2c e7 4e 50 be
c0 15 1b 01 d9 c0 bd b4 20 87 42 be d4 e2 23 a2
b6 84 22 4c cf 74 cd 4f 23 06 54 e6 c2 0f 2d bd
e5 81 f4 c6 de 15 59 f1 68 a4 a5 88 16 b0 7f bf
8a 1d 98 bd 33 b4 d5 71 22 93 81 af e0 cc ce 12
Total: 1994024 bits
00 20 00 f4 c3 b7 6d c2 31 24 92 dc 24 a7 c9 25 ae 24
b5 c4 85 88 40 be c4 92 46 25 79 2f c4 af 25 f8 92 49
24 92 64 c9 92 49 30 b1 24 92 49 24 2c 49 24 92 49 0b
12 49 24 92 42 c4 92 49 24 92 49 ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
57 23 62 3a e4 3d 8c f1 12 8d a5 40 3b 70 d6 9b
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff …
Decoding Trie: 2560 bits
12 49 62 8d 6f d4 52 f6 7f d5 11 7c ca 07 dd e3
dc 1c 7f c4 a4 69 77 6e 5e 60 db 5a 69 01 95 c8
d7 2e 57 62 b7 8e 5c 51 f9 70 55 1b 7c ba 68 bc
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
Image data: 1991464 bits

Compression Model #2: Self-Extracting Bits
To keep things conceptually simpler, let’s package the compressed data plus decoder into a single self-extracting .java file.
● Bitstream on the left generates bitstream on the right.
SelfExtractingHugPlant.java
70 75 62 6c 69 63 20 63 6c 61 73 73 20 53 65 6c
66 45 78 74 72 61 63 74 69 6e 67 48 75 67 50 6c
61 6e 74 20 7b 0d 0a … 74 68 65 20 70 61 73
73 63 6F 64 65 20 69 73 20 68 75 67 39 31 38 32
37 78 79 7A 2E 65 75 7a c0 09 eb cd d4 2a 55 9f
d8 98 d1 4e e7 97 56 58 68 0c 7a 43 dd 80 00 7b
11 58 f4 75 73 77 bc 26 01 e0 92 28 ef 47 24 66
9b de 8b 25 04 1f 0e 87 bd 87 9e 03 c9 f1 cf ad
fa 82 dc 9f a1 31 b5 79 13 9b 95 d5 63 26 8b 90
5e d5 b0 17 fb e9 c0 e6 53 c7 cb dd 5f 77 d3 bd
80 f9 b6 5e 94 aa 74 34 3a … 00 20 00 f4 c3
b7 6d c2 31 24 92 dc 24 a7 c9 25 ae 24 b5 c4 85
88 40 be c4 92 46 25 79 2f c4 af 25 f8 92 49 24
92 64 c9 92 49 c4 92 49 24 92 49 ff ff ff ff ff
HugPlant.bmp
42 4d 7a 00 10 00 00 00 00 00 7a 00 00 00 6c 00 00 00
00 02 00 00 00 02 00 00 01 00 20 00 03 00 00 00 00 00
10 00 12 0b 00 00 12 0b 00 00 00 00 00 00 00 00 00 00
00 00 ff 00 00 ff 00 00 ff 00 00 00 00 00 00 ff 01 00
00 00 00 00 00 00 00 00 00 00 01 00 00 00 00 00 00 00
00 00 00 00 01 00 00 00 00 00 00 00 00 00 00 00 01 00
00 00 00 00 00 00 00 00 00 00 00 00 00 00 ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
2,037,424 bits
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
8,389,584 bits
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff …
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff ff
ff ff ff ff ff ff ff ff ff ff ff ff ff ff…

Compression Model #2: Self-Extracting Bits
As a model for the decompression process, let’s treat the algorithm and the compressed bitstream as a single sequence of bits.
● We’ve now seen an example: SelfExtractingHugPlant. Will discuss the implications of this model next time.
0111000001110101…
SelfExtractingHugPlant.java
HugPlant.bmp
0100001001001101…
8,389,584 bits
2,037,424 bits
Interpreter

Opening a .huf File
Of course, major operating systems have no idea what to do with a .huf file.
● Have to send over the 43,400 bits of Huffman.java code as well.
● Total size (including .java file): 2,037,424 bits.
This is an alternate justification for using compression model #2.

LZW Style Compression (Extra)

Thought Experiment
How might we compress the following bitstreams (underlines for emphasis only)?
● B=”aababcabcdabcdeabcdefabcdefgabcdefgh”? ● B=”abababababababababababababababab”?
● B=”aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa”?

The LZW Approach
Key idea: Each codeword represents multiple symbols.
● Start with ‘trivial’ codeword table where each codeword corresponds to one ASCII symbol.
● Every time a codeword X is used, record a new codeword Y corresponding to X concatenated with the next symbol.
Demo Example: http://goo.gl/68Dncw

LZW
Named for inventors Limpel, Ziv, Welch.
● Related algorithm used as a component in many compression tools, including .gif files, .zip files, and more.
● Once a hated algorithm because of attempts to enforce licensing fees. Patent expired in 2003.
Our version in lecture is simplified, for example:
● Assumed inputs were ≤ 0x7f (7 bit input) and also provided 8 bit outputs (real LZW can have variable length outputs).
● Didn’t say what happens when table is full (many variants exist).

LZW
Neat fact: You don’t have to send the codeword table along with the compressed bitstream.
● Possible to reconstruct codeword table from C(B) alone.
LZW decompression example:
http://goo.gl/fdYU9C

Lossy Compression (Extra)

Lossy Compression
Most media formats lose information during compression process:
● .JPEG images.
● .MP3 audio.
● .MP4 video.
Why?
● MP4 video: 1920 x 1080 pixels, 60 times per second, 24 bits per pixel: 0.37 gigabytes per second, 1,343 gigabytes per hour.
● Downloading a movie: 30 days at 1 MB/second.

Lossy Compression
Basic idea: Throw away information that human sensory system doesn’t care about.
Examples:
● Audio: High frequencies.
● Video: Subtle gradations of color (low frequencies). See EE20 (or perhaps 16A/16B?) for more.

Summary
Compression: Make common bitstreams more compact. Huffman coding:
● Represents common symbols as codeword with fewer bits.
● Uses something like Map for compression.
● Uses something like TrieMap for decompression.
LZW:
● Represents multiple symbols with a single codeword.
● Uses something like TrieMap for compression.
● Uses something like Map for decompression.