Image_Compression_Algorithms

Implementation and Comparison of image compression algorithms

🚀 Interactive Dashboard

This project includes a Streamlit dashboard for interactive compression algorithm comparison!

Quick Start

# 1. Navigate to project root
cd Image_Compression_Algorithms

# 2. Activate virtual environment
.\venv\Scripts\Activate.ps1

# 3. Install dashboard dependencies (one-time)
pip install streamlit pandas

# 4. Launch dashboard
cd dashboard
streamlit run app.py

The dashboard will open automatically in your browser at http://localhost:8501

Dashboard Features

• 📁 Multi-image upload - PNG, JPG, TIFF formats supported
• 🔄 Real-time compression - All 5 algorithms (Huffman, LZW, RLE, DCT, DFT)
• 📊 Metrics with lossless detection - Automatic PSNR=∞, SSIM=1.0 for perfect reconstruction
• 🖼️ Visual comparison - Side-by-side original vs compressed images
• 📈 Trade-off analysis - Interactive plots (CR vs PSNR, CR vs SSIM)
• 💾 Export capabilities - CSV download, JSON session save

Usage Example

1. Upload images from samples/kodak/ (e.g., kodim01.png)
2. Select algorithms: ✅ Huffman (lossless), ✅ DCT (lossy)
3. Adjust parameters: Huffman base=2, DCT threshold=90%
4. Click "▶️ Run Compression"
5. View results and metrics

Expected Results (kodim01.png):

• Huffman: 8:1 compression, PSNR=∞, SSIM=1.0 (lossless)
• DCT: 1.1:1 compression, ~32 dB PSNR, ~0.85 SSIM (lossy)

📖 Full documentation: See dashboard/README.md and dashboard/QUICKSTART.md

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🎉 Project Status: Converted to Uniform Python Package

This repository has been successfully converted from Jupyter notebooks to a structured Python project while preserving all original algorithm implementations verbatim.

📁 New Structure

• image_compression_project/ - Main Python package with:

  • original_algorithms/ - Verbatim algorithm implementations from notebooks
  • wrappers/ - Thin CLI wrappers for easy command-line access
  • tests/ - Comprehensive verification and unit tests
  • Full documentation and setup files

• repo_backup_before_conversion/ - Complete backup of original files

• Original notebooks preserved in lossless/ directory

• Documentation files: conversion_log.txt, results_check.txt, cleanup_log.txt

🚀 Quick Start

# Navigate to the package
cd image_compression_project

# Install dependencies
pip install -r requirements.txt

# Run verification tests
python tests/verify_basic.py

# See detailed documentation
cat README.md

✅ Verification Status

All tests PASSED ✓

• 6/6 basic algorithm tests passed
• Lossless algorithms: Bit-exact reconstruction verified
• Original code preserved without modifications
• Wrapper equivalence confirmed

📖 Documentation

• Project README - Complete usage guide
• Conversion Log - Detailed conversion process
• Verification Results - Test results and quality metrics
• Cleanup Log - File preservation record

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Original README Below