🖼️ Image Compression

🧠 K-Means Powered Smart Image Optimizer built with Flask

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✨ Overview

Image Compression is a modern Flask web app that reduces image size using K-Means color quantization.

Instead of storing millions of unique colors, the algorithm intelligently groups similar colors into clusters — dramatically shrinking file size while preserving visual quality.

📦 From a simple Jupyter Notebook experiment, this project has been upgraded into a fully interactive web application with:

• ✔ Upload
• ✔ Compress
• ✔ Compare
• ✔ Download
• ✔ View metrics

All in seconds.

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🎯 Demo Flow

Upload Image
      ↓
K-Means Clustering (Color Quantization)
      ↓
Rebuild using K colors
      ↓
Show Preview + Compression Stats
      ↓
Download Optimized Image

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📸 Screenshots

💻 Interface

Result: Before vs After

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🔥 Features

🖼️ Image Processing

• K-Means color compression
• Adjustable cluster count (8 – 64 colors)
• RGB → cluster → reconstruct pipeline
• Automatic JPEG optimization

📊 Smart Metrics

• Unique original colors count
• Original file size
• Compressed file size
• Compression percentage
• Side-by-side comparison

💎 UI/UX

• Glassmorphism design
• Gradient theme
• Smooth animations
• Loading state
• Mobile responsive
• Bootstrap powered

⚡ Backend

• Flask routing
• Static file handling
• Secure uploads
• Fast NumPy processing

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🧠 How It Works (Simple)

Step 1 — Convert pixels

Image → reshape to:

(height × width, 3)

Each pixel becomes:

[R, G, B]

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Step 2 — Apply K-Means

Group pixels into K clusters

Example:

16 million colors → reduced palette ( 8, 16, 32, 64 )

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Step 3 — Rebuild image

Replace each pixel with its cluster center.

Result:

Smaller memory + Similar visual quality

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🏗️ Tech Stack

Layer	Tech
Backend	Flask
ML Algorithm	Scikit-Learn KMeans
Image Processing	OpenCV
Math	NumPy
Frontend	HTML + Bootstrap + CSS
Icons	FontAwesome

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📂 Project Structure

image-compression/
│
├── app.py
├── requirements.txt
│
├── static/
│   ├── uploads/
│   └── outputs/
│
├── templates/
│   └── index.html
│
└── README.md

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⚙️ Installation

1️⃣ Clone repo

git clone https://github.com/SACHIN-S-2004/Image-Compression.git
cd Image-Compression

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2️⃣ Install dependencies

pip install -r requirements.txt

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3️⃣ Run app

python app.py

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4️⃣ Open browser

http://127.0.0.1:5000

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📈 Example Results

Metric	Value
Original Colors	48,231
After K=16	16
Size Before	820 KB
Size After	210 KB
Saved	74%

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🚀 Future Improvements

• Drag & drop upload
• Multiple images batch compression
• Slider for real-time K selection
• React frontend version
• Auto cluster suggestion

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🎓 Learning Outcomes

This project demonstrates:

• ✔ Unsupervised Learning (K-Means)
• ✔ Image processing fundamentals
• ✔ Flask backend development
• ✔ Practical ML deployment

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⭐ If you like this project

Give it a star — it helps a lot!