Huffman Compressor

A fully functional file compression tool implementing Huffman coding algorithm from scratch in Go. This project demonstrates lossless data compression using binary trees, priority queues, and bit manipulation.

📋 Table of Contents

• About
• Features
• How It Works
• Installation
• Usage
• Technical Implementation
• Performance
• Learning Journey
• Future Enhancements
• Acknowledgments

🎯 About

This project was inspired by Coding Challenges and implements the classic Huffman coding algorithm for lossless data compression. Built entirely from scratch without using compression libraries, this tool showcases fundamental computer science concepts including:

• Binary tree data structures
• Priority queues (min-heap)
• Greedy algorithms
• Bit manipulation
• File I/O streaming

✨ Features

• Lossless Compression: Perfectly reconstructs original files
• Streaming Architecture: Memory-efficient processing for large files
• Binary Tree Implementation: Custom Huffman tree with optimal prefix codes
• Priority Queue: Hand-rolled min-heap using generics
• Bit-Level Operations: Efficient bit packing and unpacking
• Comprehensive Testing: 30+ unit and integration tests
• Edge Case Handling: Single character files, empty files, binary data

🔍 How It Works

Compression Process

Input File
    ↓
1. Analyze character frequencies
    ↓
2. Build Huffman tree (greedy algorithm)
    ↓
3. Generate variable-length prefix codes
    ↓
4. Write header (frequencies + metadata)
    ↓
5. Encode data using generated codes
    ↓
6. Pack bits into bytes
    ↓
Compressed File (.hf)

Decompression Process

Compressed File (.hf)
    ↓
1. Read and parse header
    ↓
2. Rebuild Huffman tree from frequencies
    ↓
3. Read compressed bits
    ↓
4. Traverse tree bit-by-bit
    ↓
5. Decode characters at leaf nodes
    ↓
Original File Restored

📦 Installation

Prerequisites:

• Go 1.21 or higher

Clone and Build:

# Clone the repository
git clone https://github.com/yourusername/huffman-compressor.git
cd huffman-compressor

# Build the binary
go build -o huffman cmd/main.go

# Or install directly
go install

🚀 Usage

Compress a File

# Basic compression
./huffman -compress -input file.txt -output file.hf

# The tool will display compression statistics:
# === Compression Statistics ===
# Original size:    1000 bytes
# Compressed size:  650 bytes
# Compression ratio: 65.00%
# Space saved:      350 bytes

Decompress a File

# Decompress back to original
./huffman -decompress -input file.hf -output restored.txt

# Verify files are identical
diff file.txt restored.txt  # Should show no differences

Examples

# Compress a text file with repetitive content (best compression)
./huffman -compress -input alice.txt -output alice.hf

# Compress a binary file
./huffman -compress -input image.bin -output image.hf

# Decompress
./huffman -decompress -input alice.hf -output alice_restored.txt

🏗️ Technical Implementation

File Format Specification

Compressed File Structure (.hf):

[HEADER]
  - Magic Number (2 bytes): "HF"
  - Original Size (8 bytes): uint64
  - Unique Characters (1 byte): uint8 (max 255)
  - Padding Bits (1 byte): uint8 (0-7)
  - Frequency Entries (N × 5 bytes):
      - Character (1 byte)
      - Frequency (4 bytes): uint32

[COMPRESSED DATA]
  - Variable-length encoded bits packed into bytes

Key Data Structures

1. Priority Queue (Min-Heap)

// Generic implementation with custom comparators
type PriorityQueue[T any] struct {
    heap *MinHeap[T]
}

2. Huffman Tree Node

type HuffmanNode struct {
    char      byte
    frequency int
    left      *HuffmanNode
    right     *HuffmanNode
    isLeaf    bool
}

3. Huffman Code

type HuffmanCode struct {
    bits   uint64  // LSB-first storage
    length int     // Number of bits
}

4. Bit Buffer (Writer)

// Streams bits to file, auto-flushing complete bytes
type BitBuffer struct {
    currentByte byte
    bitPosition int
    writer      io.Writer
}

5. Bit Reader

// Reads individual bits from byte stream
type BitReader struct {
    currentByte byte
    bitPosition int
    reader      io.Reader
}

Algorithm Complexity

• Tree Building: O(n log n) where n = unique characters
• Code Generation: O(n) tree traversal
• Compression: O(m) where m = file size
• Decompression: O(m × log n) for tree traversal
• Space: O(n) for tree + O(1) for streaming

📊 Performance

Compression Results

File Type	Size	Compressed	Ratio	Time
Text (repetitive)	1 MB	450 KB	45%	~50ms
Source code	500 KB	380 KB	76%	~30ms
Random data	1 MB	1.01 MB	101%	~60ms
Alice in Wonderland	167 KB	93 KB	56%	~15ms

Note: Compression effectiveness depends on data repetition. Random data may slightly expand due to header overhead.

Memory Efficiency

• Streaming I/O: Reads and writes in 1KB chunks
• Constant memory: O(1) additional space during encode/decode
• Tree overhead: ~5 bytes per unique character in header

📚 Learning Journey

This project was built as a learning exercise to understand:

Core Concepts Mastered

1. Data Structures

   • Binary trees (construction and traversal)
   • Priority queues (heap implementation)
   • Hash tables (frequency counting)

2. Algorithms

   • Greedy algorithms (Huffman's approach)
   • Tree traversal (DFS for code generation)
   • Prefix-free encoding

3. Systems Programming

   • File I/O streaming
   • Bit manipulation
   • Memory management
   • Error handling

4. Go Language Features

   • Generics (for priority queue)
   • Interfaces (io.Reader/Writer)
   • Defer statements
   • Table-driven tests

Key Challenges Solved

• LSB-first storage with MSB-first writing: Implemented bit reversal in WriteBits
• Single character edge case: Added dummy node for tree construction
• Padding handling: Track padding bits in header for accurate decompression
• Streaming architecture: Process large files without loading into memory
• Uint8 overflow: Limited unique characters to 255 (practical constraint)

🚀 Future Enhancements

Potential Improvements

• Adaptive Huffman Coding: Update tree on-the-fly
• Dictionary Encoding: Combine with LZ77 for better compression
• Parallel Processing: Multi-threaded compression for large files
• Progress Indicators: Show progress for large file operations
• Compression Levels: Trade speed for ratio (like gzip -1 to -9)
• Directory Compression: Archive multiple files (like tar)
• GUI Interface: Desktop app with drag-and-drop
• Benchmark Suite: Automated performance testing
• 256 Character Support: Use uint16 for NumChars field
• Streaming API: Library interface for programmatic use

Algorithm Variants

• Canonical Huffman: Simplify tree serialization
• Length-Limited Huffman: Bound code length for hardware
• Adaptive Huffman: Dynamic tree updates (LZSS + Huffman)

🧪 Testing

Run the comprehensive test suite:

# Run all tests
go test ./... -v

# Run with coverage
go test ./... -cover

# Run specific test package
go test ./test/compress_test.go -v

# Run benchmarks
go test ./... -bench=. -benchmem

Test Coverage:

• Unit tests: Priority queue, tree building, code generation
• Integration tests: Full compress/decompress round-trips
• Edge cases: Empty files, single characters, binary data
• Error handling: Corrupted files, invalid headers

📁 Project Structure

huffman-compressor/
├── cmd/
│   └── main.go              # CLI entry point
├── internal/
│   ├── huffman.go           # Frequency analysis
│   ├── tree.go              # Tree construction
│   ├── encoder.go           # Code generation
│   ├── bitbuffer.go         # Bit writing
│   ├── bitreader.go         # Bit reading
│   ├── header.go            # File format handling
│   ├── compress.go          # Main compression logic
│   ├── decompress.go        # Main decompression logic
│   └── priority_queue.go    # Min-heap implementation
├── test/
│   ├── *_test.go            # Comprehensive test suites
└── README.md

🤝 Contributing

This is a learning project, but contributions are welcome! Feel free to:

• Report bugs
• Suggest features
• Submit pull requests
• Share improvements

📝 License

MIT License - feel free to use this code for learning and projects.

🙏 Acknowledgments

• Coding Challenges by John Crickett for the project inspiration
• David Huffman for the algorithm (1952)
• Introduction to Algorithms (CLRS) for theoretical foundation
• The Go community for excellent documentation

📧 Contact

Created by Syed Sibteali Baqar - sibteali786@gmail.com

Project Link: https://github.com/sibteali786/huffman-compressor

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⭐ If you found this project helpful for learning, please star it on GitHub!

📖 Additional Resources

• Huffman Coding Visualization
• Original Huffman Paper (1952)
• Data Compression Explained