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Twitch and other streaming platforms rely on advanced video compression this project showcases a simplified version of those principles (DCT, quantization, motion estimation), but is not connected to Twitch itself.

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VisionCompressor

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A powerful Python-based image compression and reconstruction tool that provides efficient compression algorithms while maintaining image quality.

Features

  • Multiple Processing Modes:
    • Standard compression mode
    • BGR (Blue-Green-Red) channel processing
    • Y-channel only processing for enhanced efficiency
  • Interactive GUI for easy image manipulation
  • Batch Processing capability for multiple images
  • Visual Feedback with residual image generation
  • Customizable Parameters for compression quality

Quick Start

Prerequisites

  • Python 3.x
  • Required Python packages (install via requirements.txt)

Installation

  1. Clone the repository:

    git clone https://github.com/YounesBensafia/VisionCompressor.git
    cd VisionCompressor
  2. Install dependencies:

    pip install -r requirements.txt

Usage

GUI Interface

Launch the graphical interface for interactive compression:

python main.py

Project Structure

VisionCompressor/
├── src/              # Source code
│   ├── gui.py        # GUI implementation
│   ├── main.py       # Standard compression
│   ├── main_BGR.py   # BGR processing
│   └── main_with_y_channel_only.py
├── images/           # Input images directory
│   ├── reconstructed/  # Reconstructed images
│   └── residuals/     # Residual images
├── tests/
│
└── pyproject.toml 

Input/Output

  • Input Images: Place your images in the images/ directory
  • Output:
    • Reconstructed images are saved in output/reconstructed/
    • Residual images are saved in output/residuals/

Contributing

Contributions are welcome! Feel free to:

  • Open issues for bugs or enhancement suggestions
  • Submit pull requests with improvements
  • Share feedback about the tool

About

Twitch and other streaming platforms rely on advanced video compression this project showcases a simplified version of those principles (DCT, quantization, motion estimation), but is not connected to Twitch itself.

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