---
title: Image Sampling and Quantization Demo
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.39.0
app_file: app.py
pinned: false
license: mit
---

# Interactive Image Sampling and Quantization Demo

An educational demonstration showing how sampling and quantization impact image visualization and storage.

## What You'll Learn

This interactive demo teaches fundamental concepts in digital image processing:

### 1. **Spatial Sampling**
- How reducing sampling grid size makes images more pixelated
- The direct relationship between pixel count and file size
- Visual impact of resolution reduction

### 2. **Quantization (Bit Depth)**
- How bit depth controls the number of colors/gray levels
- The trade-off between image quality and storage
- Visual degradation as bits per pixel decrease

### 3. **Image Compression**
- Differences between JPEG and PNG compression
- How JPEG's lossy compression creates blocking artifacts
- Comparison of file sizes across different compression methods

## Features

- **Interactive Sliders**: Real-time adjustment of sampling rate and bit depth
- **Live File Size Estimates**: See how changes affect storage requirements
- **Side-by-Side Comparisons**: Original vs. processed images
- **Compression Artifacts**: Visualize JPEG blocking effects
- **Educational Insights**: Learn the theory behind each concept

## Local Development

### Setup

```bash
# Clone the repository
git clone <your-repo-url>
cd sampling-quantization

# Install dependencies
pip install -r requirements.txt
```

### Run Locally

```bash
# Simple run
streamlit run app.py

# Or use the provided script
chmod +x run_simple.sh
./run_simple.sh
```

The app will be available at `http://localhost:8501`

## Deployment to Hugging Face Spaces

This app is designed to be deployed to Hugging Face Spaces:

1. Create a new Space on [Hugging Face](https://huggingface.co/spaces)
2. Choose "Docker" as the SDK
3. Push this repository to your Space

Or use the deployment script:

```bash
chmod +x deploy.sh
./deploy.sh
```

## Educational Use

This demo is designed for:
- Graduate image analysis courses
- Computer vision fundamentals
- Digital image processing tutorials
- Self-paced learning about image storage

### Key Concepts Covered

- **Nyquist Sampling Theorem**: Understanding sampling limits
- **Bit Depth**: Relationship between bits and color/gray levels
- **File Size Calculation**: Width x Height x Bits per pixel / 8
- **Lossy vs. Lossless Compression**: JPEG vs. PNG trade-offs
- **Blocking Artifacts**: DCT-based compression effects

## Project Structure

```
sampling-quantization/
├── app.py                  # Main Streamlit application
├── requirements.txt        # Python dependencies
├── README.md              # This file
├── packages.txt           # System dependencies for HF Spaces
├── .python-version        # Python version specification
├── pyproject.toml         # Project metadata
├── run_simple.sh          # Local development script
├── deploy.sh              # Deployment helper script
└── sample_images/         # Example images (optional)
```

## License

MIT License - feel free to use for educational purposes.

## Credits

Inspired by interactive teaching tools for computer vision education.