| --- |
| 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 |
|
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| This interactive demo teaches fundamental concepts in digital image processing: |
|
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| ### 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 |
|
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| 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 |
|
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| MIT License - feel free to use for educational purposes. |
|
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| ## Credits |
|
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| Inspired by interactive teaching tools for computer vision education. |
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|
