huffman assignment intro · huffman assignment intro james wei professor peck april 2, 2014 slides...

Huffman Assignment Intro

James Wei Professor Peck April 2, 2014

Slides by James Wei

Outline •  Intro to Huffman •  The Huffman algorithm •  Classes of Huffman •  Understanding the design •  Implementation •  Analysis •  Grading •  Wrap-up

Intro to Huffman •  Huffman is a compression algorithm •  Works by examining characters in a file—

those appearing more frequently are replaced with shortcuts and those appearing less frequently with “longcuts”

•  We can then rewrite a file with different bit encodings for each character, such that the overall length is shorter

•  Of course, must also store the bit mappings

Intro to Huffman •  You will be writing code to do the following: •  Read a file and count the number of appearances

of every character •  Create a Huffman tree/encodings from the counts •  Write a header that contains the Huffman tree

data to the compressed file •  Write a compressed file •  Read the header to recreate the Huffman tree •  Uncompress a file

Intro to Huffman •  A few definitions before we proceed: •  Bit – a 0 or 1; you will be reading individual bits

using the provided utility classes •  Character – a chunk of 8bits which we will store in

an int; do not use the byte data type in this assignment; sometimes referred to as a word

•  Huffman encoding – the sequence of bits that a character is mapped to and will be replaced with in the compressed file

•  PSEUDO_EOF – a character that indicates the end of file (EOF), must be written to the compressed file and read when uncompressing

•  Magic number – a special character indicating that a file is a Huffman compressed file

Intro to Huffman •  A brief note on bits: •  You will be working with individual bits here •  We will consider a chunk of 8 bits to be a

character, which could be a letter, number, whitespace, or symbol

•  The 8 bits can represent any number from 0-255; these numbers correspond with an ASCII encoding (see: http://www.asciitable.com/)

•  We will represent a character using its 8 bits stored in an int, which gives us a value of 0-255 that corresponds with an ASCII code

Intro to Huffman •  A brief note on bits: •  For example, if we take the letter ‘A’ •  ‘A’ has an ASCII code of 65 •  In binary, 65 == 0100 0001 •  When we read n bits, we consider those bits to be

binary for some number, which is stored in an int •  So if the character in my file is ‘A’, and we read

in 8 bits, we will get an int equally 65 (more on this later)

•  When we write, we will write *one* bit at a time

The Huffman Algorithm •  The algorithm rewrites bit encodings so that

frequently used characters can be written in fewer bits

•  For example, if I have a file that reads: “gggg”

•  Then my bit representation of this is: 01100111 01100111 01100111 01100111

•  I create a shortcut: “g” => 1

•  And my new file is much shorter! “gggg” => 1111

The Huffman Algorithm •  How do we create encodings that do not

conflict with each other? •  Create a Huffman tree! •  Overview of the process: •  Count the number of appearances for every

character in the file •  Arrange the characters as leaf nodes in a tree,

with the least frequent furthest from the root •  Create the encoding for each character by tracing

a path to its node from the root

The Huffman Algorithm •  Count the number of appearances for every

character in the file. •  Pretty straightforward… •  For this example we’ll use these counts:

Character Count

A 29

B 14

C 9

D 17

E 45

F 11

G 5

The Huffman Algorithm •  Arrange the characters as leaf nodes in a tree,

with the least frequent furthest from the root •  Start by picking lowest two counts—join them

together as sibling nodes •  Next, create a parent node for the two, and

weight the parent as the combined weights of its children

•  Add the parent node to the pool of nodes to choose from, then repeat until there is only one node left—that’s the root

Character Count

A 29

B 14

C 9

D 17

E 45

F 11

G 5

The Huffman Algorithm

G (5) C (9)

Character Count

A 29

B 14

C 9

D 17

E 45

F 11

G 5

G/C 14


G (5) C (9)

(14)

Character Count

A 29

B 14

C 9

D 17

E 45

F 11

G 5

G/C 14


G (5) C (9)

(14) F (11) B (14)

(25)

Character Count

A 29

B 14

C 9

D 17

E 45

F 11

G 5

G/C 14

F/B 25


G (5) C (9)

(14) F (11) B (14)

(25)

Character Count

A 29

B 14

C 9

D 17

E 45

F 11

G 5

G/C 14

F/B 25

14/D 31


G (5) C (9)

(14)

F (11) B (14)

(25) D (17)

(31)

Character Count

A 29

B 14

C 9

D 17

E 45

F 11

G 5

F/B 25

14/D 31

25/A 54


G (5) C (9)

(14)

F (11) B (14)

(25) D (17)

(31)

A (29)

(54)

Character Count

A 29

B 14

C 9

D 17

E 45

F 11

G 5

14/D 31

25/A 54

31/E 76


G (5) C (9)

(14)

F (11) B (14)

(25) D (17)

(31)

A (29)

(54) E (45)

(76)

Character Count

A 29

B 14

C 9

D 17

E 45

F 11

G 5

25/A 54

31/E 76

54/76 130


G (5) C (9)

(14) D (17)

(31) E (45)

(76)

(130)

F (11) B (14)

(25) A (29)

(54)

The Huffman Algorithm •  Create the encoding for each character by

tracing a path to its node from the root •  Starting from the root, trace the path to each leaf •  Every time you go left, append a 0, right, a 1 •  The result at each leaf is its Huffman encoding

Character Encoding

A

B

C

D

E

F

G


G (5) C (9)

(14) D (17)

(31) E (45)

(76)

(130)

F (11) B (14)

(25) A (29)

(54)

Character Encoding

A 01

B

C

D

E

F

G


G (5) C (9)

(14) D (17)

(31) E (45)

(76)

(130)

F (11) B (14)

(25) A (29)

(54)

Character Encoding

A 01

B 001

C

D

E

F

G


G (5) C (9)

(14) D (17)

(31) E (45)

(76)

(130)

F (11) B (14)

(25) A (29)

(54)

Character Encoding

A 01

B 001

C 1001

D

E

F

G


G (5) C (9)

(14) D (17)

(31) E (45)

(76)

(130)

F (11) B (14)

(25) A (29)

(54)

Character Encoding

A 01

B 001

C 1001

D 101

E

F

G


G (5) C (9)

(14) D (17)

(31) E (45)

(76)

(130)

F (11) B (14)

(25) A (29)

(54)

Character Encoding

A 01

B 001

C 1001

D 101

E 11

F

G


G (5) C (9)

(14) D (17)

(31) E (45)

(76)

(130)

F (11) B (14)

(25) A (29)

(54)

Character Encoding

A 01

B 001

C 1001

D 101

E 11

F 000

G


G (5) C (9)

(14) D (17)

(31) E (45)

(76)

(130)

F (11) B (14)

(25) A (29)

(54)

Character Encoding

A 01

B 001

C 1001

D 101

E 11

F 000

G 1000


G (5) C (9)

(14) D (17)

(31) E (45)

(76)

(130)

F (11) B (14)

(25) A (29)

(54)

Classes of Huffman •  The relevant classes of this assignment: •  Huff: main class to run the Huffman program •  SimpleHuffProcessor: what you will complete •  HuffMark: benchmarking program to be used for

your analysis •  IHuffConstants: contains constants that you will use

in SimpleHuffProcessor •  Diff: utility tool that compares two files and returns

whether they are byte-equivalent •  TreeNode: a node in the Huffman tree—implements

Comparable and compares by weight •  Bit(In/Out)putStream: two classes that read and write

bit-by-bit, extends InputStream/OutputStream

Understanding the design •  The only class you need to modify is

SimpleHuffProcessor, which implements IHuffProcessor

•  Within this class are three methods: •  preprocessCompress: to be called before a

compression—reads a file and creates Huffman encodings

•  compress: uses Huffman encodings to write a compressed file

•  uncompress: reads a compressed file, uses the header to build Huffman encodings, and decodes the file

Implementing preprocess •  The first method we need to implement:

int preprocessCompress(InputStream) throws IOException

•  This method must: •  Find the weights of all characters in the file •  Build a Huffman tree from the weights •  Traverse the Huffman tree to determine

encodings for every character •  Find and return the total number of bits saved

Implementing compress •  The first method we need to implement:

int compress(InputStream, OutputStream, boolean) throws IOException

•  This method must: •  Check that compression actually saves bits (unless

force compression is true) •  Write a magic number to the beginning of the file •  Write the weight of each character to the file •  Read the original file and write each character’s

Huffman encoding to the new file •  Write a PSEUDO_EOF to the end of the file •  Return the number of bits written to file

Implementing uncompress •  The first method we need to implement:

int uncompress(InputStream, OutputStream) throws IOException

•  This method must: •  Check for the existence of the magic number •  Read the weights from the header and rebuild the

Huffman tree •  Read one bit at a time, traversing down the tree until

we hit a leaf, and write the character represented by the leaf to the file

•  Return the number of bits written to file

Preprocess in detail •  How do we determine character weights? •  Need to use the InputStream we are given •  Create a new BitInputStream using the IS as an

argument to its constructor •  Create a data structure to store the weights (array,

map, your choice) •  Read IHuffConstants.BITS_PER_WORD bits

using our BitInputStream readBits method; this method returns an int storing the value of the bits

•  Update the weight of the returned character

Preprocess in detail •  Some pseudocode to get you started: preprocessCompress(...) {

BitInputStream bis = new BIS();

int[] weights = new int[];

int current = bis.readBits(...);

while (successfully read bits) {

// update weights for current

// read next character

}

}

Preprocess in detail •  How do we build the Huffman tree with code? •  After counting the weights of each character,

create a TreeNode for each one •  What data structure should we store our

TreeNodes in so that we can easily obtain the two with the smallest weights each iteration?

•  When are we done building the tree?

Preprocess in detail •  Some pseudocode to get you started:

preprocessCompress(...) { PriorityQueue<TreeNode> pq = new PQ<>(); for (every character) { pq.add(new TreeNode(character, weight)); } while (pq not empty) { if (pq only has one node) break; TreeNode left = pq.pop(); TreeNode right = pq.pop(); // create new TreeNode as parent, add to pq } // get remaining node and set as root }

Compress in detail Check for

positive bits saved

Write magic number

Write weights of each char

Attempt to read one character from file

If read successfully

Lookup encoding

Write encoding

Write PSEUDO_EOF

Return # bits written

YES

NO

Compress in detail •  If you are interested in extra credit… •  The header you write is simply a list of all character

weights •  For extra credit, additionally write code that creates

the header from a preorder traversal of the tree, instead of weights, so that uncompress can build the tree faster.

•  For full credit you need *BOTH* header implementations (but don’t write both to the compressed file!)

•  You will also need to write about the differences in header implementations in your analysis to get full points for the extra credit

Uncompress in detail

Check magic number

Read weights from header

Build tree; set current to

root

Read single bit from the input stream

Move to left child

If node is leaf Move to right child

If value is PSEUDO_EOF

Return # bits written

YES

NO

Zero

One

Write value to file; set current

to root

YES

NO

BitInputStream/BitOutputStream

•  Don’t need to know the inner workings of these classes—just how to use them

•  BitInputStream: mainly you will use int readBits(int)

•  The single argument is how many bits to read

•  BitOutputStream: mainly you will use int writeBits(int, int)

•  The first argument is how many bits to write •  The second argument is what value to write

Analysis •  It’s the last assignment—of course there’s an

analysis part! •  Run HuffMark on both the calgary directory

and the waterloo directory; you may want to modify HuffMark to display more data (focus on the compress method, for the most part)

•  You may also want to modify HuffMark to not skip over files with the .hf extension—you will want to compress already compressed files for your analysis!

Analysis •  Once you’re comfortable with using HuffMark,

answer the following in your write-up: •  Which compresses more, binary or text files? •  How much additional compression do you get by

compressing an already compressed file? When does this become ineffective?

•  Can you design a file that should compress a lot? When is it no longer worthwhile to keep compressing that file?

•  If you did the extra credit, compare the performance and effectiveness of your two implementations

Grading •  Your grade is based on: •  25% - compressing/decompressing text files •  25% - compressing/decompressing binary files •  20% - robustness, basic error handling

•  Examples include not compressing if bits saved is negative (unless force compression is active), terminating if no magic number/pseudo-EOF found

•  In cases where you end decompression early, do not worry if the file has already been created and is not deleted—you will not be penalized for that

•  10% - coding style and documentation •  20% - full and complete analysis

Wrap-up •  Recommended plan of attack: •  Read over the assignment write-up; look at the

snarf code and understand the basic flow of the program, as well as what you will be adding

•  Implement preprocessCompress •  Test by creating a short dummy text file and

running preprocess on it—you can check your tree for correctness by writing a brief recursive algorithm to print out its preorder traversal

Wrap-up •  Recommended plan of attack: •  Implement compress •  Test by compressing some files and checking to

see that they have shrunk in size; try creating a file that is very short, so that the compressed file will be larger than the original—does your program detect this and abort compression? •  If you open a .hf file in a text editor, you should see a

lot of gibberish •  Not too much testing you can really do here without

uncompress, unfortunately

Wrap-up •  Recommended plan of attack: •  Implement uncompress •  Test by uncompressing some .hf files from your

compress method; check the following: •  Does your .unhf file match the original? Use Diff to

check that they are exactly the same. •  If you try to uncompress a file that was NOT

compressed with your code, does uncompress correctly detect the missing magic number and abort?

•  Comment out your code adding a pseudo-EOF to the end of the .hf file; does uncompress detect this and abort?

Good luck!

Start early!

huffman assignment intro · huffman assignment intro james wei professor peck april 2, 2014 slides...

Documents