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CMSC 100 Storing Data: Huffman Codes and Image Representation

CMSC 100 Storing Data: Huffman Codes and Image Representation. Professor Marie desJardins Tuesday, September 18, 2012. Data Compression: Motivation. Memory is a finite resource: the more data we have, the more space it takes to store

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CMSC 100 Storing Data: Huffman Codes and Image Representation

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  1. CMSC 100Storing Data: Huffman Codes and Image Representation Professor Marie desJardinsTuesday, September 18, 2012 CMSC 100 -- Data Compression

  2. Data Compression: Motivation • Memory is a finite resource: the more data we have, the more space it takes to store • Same with bandwidth: the more data we need to send, the more time it takes • Data compression can reduce space and bandwidth • Lossless compression: Store the exact same data in less space • Lossy compression: Store an approximation of the data in less space CMSC 100 -- Data Compression

  3. Time and Space Tradeoffs • Data compression trades (computational) time for space and bandwidth: • It takes time to convert the original data D to the compressed format DC • It takes time to convert compressed data DC back to a viewable format D’ • Compression ratio: • Space savings: CMSC 100 -- Data Compression

  4. Lossless vs. Lossy Compression • Lossless: Save space without losing any information • Take advantage of repetition and self-similarity (e.g., solid-color regions in an image) • Lossy: Save space but lose some information • Lose resolution or detail (e.g., “pixillate” an image or remove very high/low frequencies in a sound file) CMSC 100 -- Data Compression

  5. Encoding Strategies • Run-length encoding: replace n instances of object x with the pair of numbers (n,x) • Frequency-dependent encoding: use shorter representations (fewer bits) for objects that appear more frequently in a document • Relative or differential encoding: when x is followed by y, represent y by the difference y-x (which is often small in images etc. and can therefore be represented by a short code) • Dictionary encoding: Create an index of all of the objects (e.g., words) in a document, then replace each object with its index location (can save space if there is a lot of repetition) CMSC 100 -- Data Compression

  6. Image and Sound Formats • Images • Row-by-row bitmaps in different color spaces: • RGB (one byte per color = 24 bits = 17M different colors), a.k.a “True Color” (used in JPEG formats) (How much storage for one True Color 2Kx3K digital camera image?) • Color palette: Use only one byte to index 256 of the 17M 24-bit colors (used in GIF formats) (How much storage for one 24-bit color 200x300 image on a website?) • Variable resolution provides different image sizes and levels of fidelity to an original (continuous or very high-resolution digital) image • Sound • Convert continuous sound to digital by sampling (variable-rate) • Each sample can be represented with varying levels of resolution (“bit depth”) (MP3: 44K samples/second, 16 bits/sample – how much storage for one minute of sound?) CMSC 100 -- Data Compression

  7. Compression Ratio: Example • Suppose I have a 2M .PNG (bitmap) image and I store it in a compressed .JPG file that is 187K. What is the compression ratio? What is the space savings? CMSC 100 -- Data Compression

  8. Huffman Coding • Lossless frequency-based encoding • Huffman coding is (space-)optimal in the sense that if we need the exact distribution (frequency) of every object, we will be able to represent the document in the shortest possible number of bits • Downside: It takes a while to compute • Goal #1: Length of each object should be related to its frequency • Specifically: length is proportion to the negative log of the frequency • Goal #2: Code should be unambiguous • Since objects will be encoded at different lengths, as we read the bits, we need to know when we’ve reached the end of one object and should begin processing the next one • This type of code is called a prefix code CMSC 100 -- Data Compression

  9. Using a Prefix Code How would you represent“HELLO” using this code? 0 1 0 1 0 1 A E 0 1 0 1 H L O 0 1 Note: By convention, the left branch is 0;the right branch is 1 C S CMSC 100 -- Data Compression

  10. Interpreting a Prefix Code 0 1 0 1 0 1 A E 0 1 0 1 H L O 0 1 What does “1110000110110111110”mean in this code? C S CMSC 100 -- Data Compression

  11. Interpreting a Prefix Code 0 1 0 1 0 1 A E 0 1 0 1 H L O 0 1 C What does “1110000110110111110”mean in this code? C S CMSC 100 -- Data Compression

  12. Interpreting a Prefix Code 0 1 0 1 0 1 A E 0 1 0 1 H L O 0 1 C What does “1110 | 000110110111110”mean in this code? C S CMSC 100 -- Data Compression

  13. Interpreting a Prefix Code 0 1 0 1 0 1 A E 0 1 0 1 H L O 0 1 C H What does “1110 | 000110110111110”mean in this code? C S CMSC 100 -- Data Compression

  14. Interpreting a Prefix Code 0 1 0 1 0 1 A E 0 1 0 1 H L O 0 1 C H O O S E What does “1110 | 000 | 110 | 110 | 1111 | 10”mean in this code? C S CMSC 100 -- Data Compression

  15. You try it! 0 1 0 1 0 1 0 1 0 1 0 1 0 1 A SPC O Y 0 1 0 1 0 1 0 1 L T W E R 0 1 0 0 1 1 ! C M P S U Decode the Message:0111110010100101011011100011110111110110 010 00111111110 010 0110001110 010 0110001110 010 0110001110 010 0001100000100100000000110 010 011111001000000 01110 CMSC 100 -- Data Compression

  16. Encoding Algorithm • Frequency distribution: • Set of k objects, o1...ok • Number of times of each object appears in the document, n1...nk • Construct a Huffman code as follows: • Pick the two least frequent objects, oi and oj • Replace them with a single combined object, oij, with frequency ni+nj • If there are at least two objects left, go to step 1 • Visually: • Each of the original objects is a leaf (bottom node) in the prefix tree • Each combined objects represents a 0/1 split where the “children” are the two objects that were combined • In the last step, we combine two subtrees into a single final prefix tree CMSC 100 -- Data Compression

  17. Encoding Example • SHE SELLS SEASHELLS BY THE SEASHORE CMSC 100 -- Data Compression

  18. Encoding Example • SHE SELLS SEASHELLS BY THE SEASHORE • Frequency distribution: • A – 2 • B – 1 • E – 7 • H – 4 • L – 4 • O – 1 • R – 1 • S – 8 • T – 1 • Y – 1 • <SPC> – 5 CMSC 100 -- Data Compression

  19. Encoding Example • SHE SELLS SEASHELLS BY THE SEASHORE • Frequency distribution: • A – 2 • B – 1 • E – 7 • H – 4 • L – 4 • O – 1 • R – 1 • S – 8 • T – 1 • Y – 1 • <SPC> – 5 2 B1 O1 CMSC 100 -- Data Compression

  20. Encoding Example • SHE SELLS SEASHELLS BY THE SEASHORE • Frequency distribution: • A – 2 • B – 1 • E – 7 • H – 4 • L – 4 • O – 1 • R – 1 • S – 8 • T – 1 • Y – 1 • <SPC> – 5 2 2 3 B1 O1 R1 T1 A2 Y1 CMSC 100 -- Data Compression

  21. Encoding Example • SHE SELLS SEASHELLS BY THE SEASHORE • Frequency distribution: • A – 2 • B – 1 • E – 7 • H – 4 • L – 4 • O – 1 • R – 1 • S – 8 • T – 1 • Y – 1 • <SPC> – 5 7 4 2 2 3 B1 O1 R1 T1 A2 Y1 CMSC 100 -- Data Compression

  22. Encoding Example • SHE SELLS SEASHELLS BY THE SEASHORE • Frequency distribution: • A – 2 • B – 1 • E – 7 • H – 4 • L – 4 • O – 1 • R – 1 • S – 8 • T – 1 • Y – 1 • <SPC> – 5 7 8 4 H4 L4 2 2 3 B1 O1 R1 T1 A2 Y1 CMSC 100 -- Data Compression

  23. Encoding Example • SHE SELLS SEASHELLS BY THE SEASHORE • Frequency distribution: • A – 2 • B – 1 • E – 7 • H – 4 • L – 4 • O – 1 • R – 1 • S – 8 • T – 1 • Y – 1 • <SPC> – 5 12 7 8 4 _5 E7 H4 L4 2 2 3 B1 O1 R1 T1 A2 Y1 CMSC 100 -- Data Compression

  24. Encoding Example • SHE SELLS SEASHELLS BY THE SEASHORE • Frequency distribution: • A – 2 • B – 1 • E – 7 • H – 4 • L – 4 • O – 1 • R – 1 • S – 8 • T – 1 • Y – 1 • <SPC> – 5 15 12 7 8 4 _5 E7 H4 L4 2 2 3 B1 O1 R1 T1 A2 Y1 CMSC 100 -- Data Compression

  25. Encoding Example • SHE SELLS SEASHELLS BY THE SEASHORE • Frequency distribution: • A – 2 • B – 1 • E – 7 • H – 4 • L – 4 • O – 1 • R – 1 • S – 8 • T – 1 • Y – 1 • <SPC> – 5 35 20 15 12 S8 7 8 4 _5 E7 H4 L4 2 2 3 B1 O1 R1 T1 A2 Y1 CMSC 100 -- Data Compression

  26. Green Eggs and Ham CMSC 100 -- Data Compression

  27. Green Eggs and Ham Symbols (not letters!) are words. Ignore spaces and punctuation. You try it! I am Sam I am Sam Sam I am That Sam-I-am! That Sam-I-am! I do not like that Sam-I-am! Do you like green eggs and ham? I do not like them, Sam-I-am. I do not like green eggs and ham. CMSC 100 -- Data Compression

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