This repository contains software and datasets for a project developed for the master degree course of Data Compression at University of Salerno.
The primary goal of this project is to perform a comparative analysis between traditional data compression techniques and Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) applied in the context of LightField Images. All the traditional techniques that will be used for the analysis are already evaluated on the datasets of this repository by previous works made from other master students of the Data Compression course. Our mission is to expand this research field in order to find new and competitive approaches for image compression and see if they can be compared with traditional and well estabilished algorithms.
- datasets, the datasets folder contains all the data used for the evaluation of the algorithms.
- src, the src folder contains all the code written for the project.
Step 1: create and install the docker image with the following commands
docker build -t IMAGE_NAME .
docker run -d -v "$(pwd):/workspace" IMAGE_NAME
docker exec -it CONTAINER_ID /bin/bash
cd workspace
Step 2: create and activate the python virtual environment
python3 -m venv data_compression
source data_compression/bin/activate
Step 3: install pip dependencies
pip3 install -r requirements.txt