DATA COMPRESSION STUDY GUIDE

Terminology and Basics

• Why compress? To save space and time.
• How does compression work? Compression takes advantage of structure within data.
• What sort of data does not compress well? Random data.
• Lossy compression can further reduce file sizes by throwing away unimportant information.
• What is the compression ratio?
• Why can no compression algorithm possibly compress all bit streams?
• What fraction of bitstreams can be compressed in half by a general-purpose algorithm?

• Takes advantage of repeated binary digits.
• How do you handle runs of length more than 2^M?

• Basic idea: Variable length codewords to represent fixed length characters. More common characters are represented by shorter codewords.
• What is a prefix-free code? Why is it important that Huffman coding use a prefix-free code? Would encoding work with a non prefix-free code? Would decoding work?
• Why is it necessary to transmit the coding trie? Why don't we have to do something similar with run length encoding or LZW?
• Why do we typically use an array for encoding and a trie for decoding?
• You do not need to know the specifics of the binary representaiton of the Huffman trie. However, you should conceptually understand the idea of transmitting/reading the trie using an preorder traversal.

• Basic idea: Fixed length codewords to represent variable length strings. More common characters are represented by shorter codewords.
• Why do we typically use a trie for encoding and an array for decoding?
• How do you handle the 'strange' case (where a codeword is seemingly not in the table during decoding)?

Recommended Problems

C level

1. Fall 2011 Final, #10b
2. Spring 2015 Final, #8
3. Textbook 5.5.3

B level

1. Fall 2011 Final, #10a
2. Fall 2014 Final, #8
3. Fall 2014 Final, #9
4. Fall 2014 Final, #11a, b
5. Textbook 5.5.13
6. Textbook 5.5.17

A level

1. Fall 2012 Final, #13
2. Fall 2014 Final, #11c