Image Compression Project

Comparative Analysis and Implementation of Lossless vs. Lossy Image Compression Algorithms

A comprehensive Python project that compares lossless (PNG) and lossy (JPEG) image compression techniques with an interactive web interface using Streamlit.

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Features

✅ Lossless Compression - PNG format with optimization
✅ Lossy Compression - JPEG format (quality=50) with optimization
✅ Batch Processing - Process multiple images at once
✅ Interactive Dashboard - Web-based UI with Streamlit
✅ Visualizations - Charts and graphs for analysis
✅ CSV Reports - Generate detailed compression reports
✅ Error Handling - Graceful handling of corrupted images
✅ Support - JPG, JPEG, PNG, and BMP formats

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Installation

1. Clone or Navigate to Project Directory

cd Image_Compression_Project

2. Install Dependencies

pip install -r requirements.txt

Or manually install:

pip install Pillow pandas streamlit plotly

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Usage

Option 1: Command Line (Console Output)

Run the standard Python script for console-based compression:

python main.py

Output:

• Compressed images in compressed_lossless/ and compressed_lossy/ folders
• CSV report in results/report.csv
• Console summary with statistics

Option 2: Interactive Web Dashboard (Recommended)

Run the Streamlit application:

streamlit run streamlit_app.py

Browser Access:

• Opens automatically at http://localhost:8501
• If not, manually navigate to the URL shown in terminal

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Streamlit Dashboard Features

📊 View Results Tab

• View all compression results from the CSV report
• Interactive data table
• Key statistics (total images, average reductions, total saved)
• Download report as CSV
• Three visualization tabs:
  • Size Comparison - Bar chart comparing original vs compressed sizes
  • Reduction % - Bar chart showing compression reduction percentages
  • Compression Ratio - Scatter plot of compression ratios

🖼️ Compress Images Tab

• Upload individual images for compression
• View before/after comparison
• Get instant compression metrics
• Download compressed versions

📁 Batch Process Tab

• Process all images in the images/ folder
• Progress bar during processing
• Automatic CSV report generation
• View results with statistics and visualizations

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Project Structure

Image_Compression_Project/
│
├── images/                          # Place your images here
│   └── (add JPG, JPEG, PNG, BMP files)
│
├── compressed_lossless/             # Lossless PNG outputs
├── compressed_lossy/                # Lossy JPEG outputs
├── results/                         # CSV reports
│   └── report.csv
│
├── main.py                          # CLI application
├── streamlit_app.py                 # Web dashboard
├── compression.py                   # Compression functions
├── analysis.py                      # Analysis functions
├── requirements.txt                 # Python dependencies
└── README.md                        # This file

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File Descriptions

compression.py

Contains compression functions:

• compress_lossless(input_path, output_path) - PNG compression with optimization
• compress_lossy(input_path, output_path) - JPEG compression with quality=50

analysis.py

Contains analysis functions:

• compression_ratio(original_size, compressed_size) - Calculates compression ratio
• size_reduction_percentage(original_size, compressed_size) - Calculates reduction %

main.py

CLI application that:

• Scans images/ folder for supported formats
• Processes each image with both compression methods
• Generates results/report.csv
• Displays formatted summary table

streamlit_app.py

Interactive web dashboard with:

• Three operational modes (View Results, Compress Images, Batch Process)
• Real-time image previews
• Interactive visualizations
• Download capabilities

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Compression Algorithms

Lossless Compression (PNG)

• Format: PNG (Portable Network Graphics)
• Optimization: PIL optimize=True
• Use Case: Lossless compression, suitable for graphics and images requiring perfect quality
• Typical Result: 2-50% size reduction (varies by image type)

Lossy Compression (JPEG)

• Format: JPEG (Joint Photographic Experts Group)
• Quality Setting: 50 (0-100 scale)
• Optimization: PIL optimize=True
• RGB Conversion: Automatic handling of transparency and color modes
• Use Case: Significant file size reduction, acceptable for photographs
• Typical Result: 50-95% size reduction

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Metrics Explained

Compression Ratio

Formula: Original Size / Compressed Size

• Ratio = 1.0 - No compression
• Ratio > 1.0 - Successfully compressed
• Ratio < 1.0 - File expanded (poor compression)

Size Reduction Percentage

Formula: ((Original - Compressed) / Original) × 100

• Positive % - File reduced in size
• Negative % - File increased in size

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Example Output

CLI Output

[1/1] Processing: black-adam-the-fire-bg.jpg
  ✓ Lossless: -127.92% reduction
  ✓ Lossy: 86.67% reduction

======= STATISTICS =======
Total images processed:        1
Average lossless reduction:    -127.92%
Average lossy reduction:       86.67%

CSV Report (results/report.csv)

Image Name,Original Size (KB),Lossless Size (KB),Lossless Compression Ratio,Lossless Reduction %,Lossy Size (KB),Lossy Compression Ratio,Lossy Reduction %
black-adam-the-fire-bg.jpg,7110.55,16206.11,0.44,-127.92,947.54,7.5,86.67

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Error Handling

The application includes robust error handling:

• Corrupted images are skipped with error message
• Unsupported formats are ignored
• Missing folders are created automatically
• Invalid inputs are validated

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Troubleshooting

Streamlit not found

pip install streamlit

PIL (Pillow) errors

pip install --upgrade Pillow

No images found

• Ensure images are placed in the images/ folder
• Check supported formats: JPG, JPEG, PNG, BMP

CSV Report not generated

• Ensure results/ folder exists (auto-created on run)
• Check disk space and file permissions

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Performance Notes

• Processing Speed: Depends on image size and quantity
• Memory Usage: Moderate (loads one image at a time)
• Disk Space: Compressed files stored simultaneously

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Future Enhancements

• Additional compression algorithms (WebP, AVIF)
• Quality slider for lossy compression
• Real-time preview updates
• Batch upload with drag-and-drop
• Advanced filtering and sorting
• Export to multiple formats

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License

This project is provided as-is for educational purposes.

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Author

Created as a comprehensive image compression analysis project.

Last Updated: June 2026