Back to Computer Networks
Data Compression
Compression algorithms and their applications in networking
Key Algorithms
Huffman, LZW, RLE, and modern compression techniques
Applications
Web compression, file storage, multimedia streaming
Interactive Tools
Compression simulators and performance analyzers
Compression Topics
Interactive Compression Tools
Compression Overview Details
Compression Fundamentals
Data Compression
Process of encoding data using fewer bits than original representation
Compression Ratio
Ratio of uncompressed size to compressed size
Formula: Compression Ratio = Original Size / Compressed Size
Example: 100KB file compressed to 25KB = 4:1 ratio
Redundancy
Repetitive or predictable patterns in data
Compression Categories
Lossless Compression
Perfect reconstruction of original data
Algorithms: Huffman Coding, LZW, Run-Length Encoding, Arithmetic Coding
Advantages: No data loss, perfect reconstruction
Applications: Text files, Executable files, Medical images
Disadvantages: Lower compression ratios
Lossy Compression
Approximate reconstruction with some data loss
Algorithms: JPEG, MP3, MPEG, H.264
Advantages: Higher compression ratios
Applications: Images, Audio, Video
Disadvantages: Quality degradation
📝 Exam Preparation Tips
Key Topics to Remember
- • Understand lossless vs lossy compression
- • Know Huffman coding algorithm steps
- • Practice compression ratio calculations
- • Remember LZW dictionary building process
- • Understand compression applications in networking
Common Exam Questions
- • "Build Huffman tree for given text" (8 marks)
- • "Calculate compression ratio" (3 marks)
- • "Explain LZW encoding steps" (6 marks)
- • "Compare lossless vs lossy compression" (5 marks)
- • "Applications of compression in networks" (4 marks)