Compression Techniques - Final Project

Features

• Supports Lossless Compression:
  • Run-Length Encoding (RLE)
  • Huffman Encoding
  • Arithmetic Encoding
• Supports Lossy Compression:
  • NU Scalar Quantization
• Interactive GUI built with PyQt5
• Real-time encoding and decoding with detailed outputs
• Clear output of compression ratios and results

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Algorithms Overview

Lossless Compression

Run-Length Encoding (RLE)

• Simplifies repetitive data patterns.
• Encodes consecutive identical elements as a single value and count.
• Example: AAAABBBCCDAA → 4A3B2C1D2A.

Huffman Encoding

• Constructs a binary tree based on character frequencies.
• Assigns shorter codes to frequent characters.
• Guarantees the shortest average code length for a given set of characters.

Arithmetic Encoding

• Represents an entire message as a single fractional value.
• Subdivides the range [0, 1) based on character probabilities.

Lossy Compression

NU Scalar Quantization

• Utilizes the LBG algorithm to quantize data into fewer levels.
• Prioritizes data size reduction at the cost of minor quality loss.

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GUI Overview

Main Features:

• Select Compression Method: Choose from the available methods.
• Input Data: Enter text or numerical data for processing.
• Encode and Decode: Perform compression and decompression operations.
• Character Probability Table: Visualize and configure probabilities for Arithmetic Encoding.

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Installation

1. Clone the repository:

   git clone https://github.com/Talalahmed409/Compression-Project.git
   cd compression-project

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the application:

   python compression_gui.py

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Screenshots

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Team Members

• Shrouq Mohammed Rashad
• Mennah Allah Yasser Nabil
• Eyad Samih Hanafi
• Fouad Essam William
• Talal Ahmed Mahmoud

Supervisors

• Dr. Mahmoud Gamal
• Eng. Mariem Nagy

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Acknowledgments

Special thanks to Alexandria University and our mentors for their guidance throughout this project.