🖼️Image compressor

Projecte-Compressió

Image compression project developed in Java. Explore the docs » View Demo · Report Bug · Request Feature

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About The Project

This project implements a basic image compression system in Java. The goal is to analyze and apply entropy coding, quantization, and prediction — the first three main stages of an image compression pipeline.

This project has been developed as part of academic exercises on image compression and processing by both Mikel Garzón Gomes and Pol Farres Ribatadalla.

Note: Entropy-related modules are still in progress.

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Built With

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Getting Started

You only need Git and Java — there are no external dependencies.

Installation

1

Clone the repository

git clone https://github.com/polfarres/Projecte-Compressi-.git

2

Compile and run with Java

javac Main.java
java Main

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Usage

The program runs in the console and displays a menu like the following:

System.out.println("Introdueix una opció (1-9 o 's'): ");
System.out.println("1.- Read Images");
System.out.println("2.- Calculate Total Entropy of Images (Order 0)");
System.out.println("3.- Conditional Entropy (Pixel Correlation)");
System.out.println("4.- Conditional Entropy of 4 Neighbor Pixels (Order 1)");
System.out.println("5.- Image Quantization");
System.out.println("6.- Image Dequantization");
System.out.println("7.- Image Prediction (after prior quantization)");
System.out.println("8.- Image Deprediction (Reconstruction)");
System.out.println("9.- Calculate Distortion Metrics (MSE and PAE) of Quantized Images");
System.out.println("-----------------------------------------------------------------------");
System.out.println("s.- Exit the Application");

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Roadmap

Projecte-Compressió

Image Compression Project

• Read and write images

• Entropy calculation

  • Entropy of order 0

  • Entropies of order 1

• Quantization

• Dequantization

• Entropy of the quantized image

• Entropy of the dequantized (lossy) image

• Prediction

• Pipeline

• Coder and decoder done

• User interface improoved

• Code refactored

• Distortion metrics (MSE and PAE)

Competition

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In this part of the project we are going to compare the different methods implemented in the previous sections. To acomplish a better results than our classmates. The improvements will be focused on the following points:

Pol Farres

• Dead Zone quantization augmented

• Adaptative Quantization

• Aritmetic Encoding Entropy

  • First degree

  • Second degree

Mikel Garzón

• Lineal function DPCM

• Median Edge Detector Predictor

• LOCO-I predictor

• GAP predictor

  • Improved MED (original)

• Smoothing Kernel implemented

• Adaptative Prediction --> knn

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We are currently focusing on the first three stages of image compression: Entropy Encoding, Quantization, and Prediction.

See the open issues for a full list of proposed features (and known issues).

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Contributing

Contributions are what make the open source community amazing. Any suggestions or improvements are greatly appreciated.

1. Fork the Project

2. Create your Feature Branch (git checkout -b feature/AmazingFeature)

3. Commit your Changes (git commit -m 'Add some AmazingFeature')

4. Push to the Branch (git push origin feature/AmazingFeature)

5. Open a Pull Request

Top contributors:

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License

Distributed under the project_license. See LICENSE.txt for more information.

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Contact

Pol Farres — GitHub Profile

Mikel Garzón — Github Profile

Project Link: https://github.com/MkProgramer33/Image-Compressor

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