Lossy Data Compression using K-Means Clustering on Retinal Images using RStudio

The massive volume of data limits the use of data on portable devices like mobile phones, as well as memory-limited devices like video game consoles. As a result, the demand for data compression methods in the field of signal and image processing is skyrocketing. Data compression required advanced ways to serve better, with a greater compression ratio, due to the rising demands, which expected more sophisticated algorithms. This article primarily focuses on image compression (retinal images) utilizing the k-means clustering technique, which is a popular unsupervised classification strategy in machine learning. The compression challenge in this study uses two retinal pictures. The most useful open-source statistical calculation tool is R programming, which is employed here with RStudio, an Integrated Development Environment (IDE). The use of R (RStudio) has had an impact across all domains of research and engineering. The test retinal images had a compression ratio of 33.3 percent, according to simulation data.