Data Compression
Chloe Evans
6 min read
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Study Guide Overview
This study guide covers data compression for the AP CSP exam, including the purpose of compression, lossy vs. lossless methods (with examples like Run-length encoding and LZW), and hybrid approaches. It emphasizes the trade-offs between compression and data quality and provides practice questions on these concepts.
#AP Computer Science Principles: Data Compression - The Night Before 🚀
Hey! Let's get you prepped and confident for tomorrow's AP CSP exam. We're going to break down data compression, making sure it's crystal clear and you're ready to ace those questions.
#Why Data Compression? 🤔
Think of data compression like packing for a trip. You want to fit as much as possible into your suitcase (storage) without taking up too much space or making it too heavy (large file size). It's all about making digital data smaller and more manageable.
- Problem: Digital data (like images and videos) is HUGE! It takes up a lot of storage space and is hard to send.
- Solution: Data compression reduces the number of bits needed to store or transmit data.
- Key Factors:
- Redundancy: How much repeated info is in the original data?
- Compression Method: Which algorithm are you using?
Think of data compression as a digital shrink ray! 💥 It makes files smaller without losing the important stuff (mostly).
#Data Compression Methods 🛠️
#Run-Length Encoding
- How it works: Replaces repeating sequences with a count and the value of the repeated data.
- Example: "AAAAABBBCCCDD" becomes "5A3B3C2D".
- Use Cases: Simple images (bitmaps), fax machines.
Run-length encoding is great for data with long runs of the same value. It's like saying "5 of these" instead of listing them all out.
#LZW Compression Algorithm
- How it works: Replaces repeating patterns with symbols.
- Use Cases: Text and images, especially GIFs.
LZW is like creating a **...
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