Lossless Image Compression using K-Means Clustering in Color Pixel Domain

Compressing images is a method for shrinking the image’s dimensions using a particular algorithm. Image compression is a solution associated with transmitting and storing large amounts of data for digital images. While image storage is necessary for medical images, satellite images, documents, and pictures, image transmission includes a variety of applications like television broadcasting, remote sensing via satellite, and other long-distance communication. These kinds of applications deal with image compression. Lossy compression and lossless compression are two separate methods for compressing images. While unneeded metadata is deleted in lossless compression to minimize file size, lossy compression permanently removes part of the original data to reduce file size. The terms “supervised learning” and “unsupervised learning” refer to two different machine learning techniques. As opposed to a non-supervised learning algorithm, which uses unlabeled input data, supervised learning uses labeled data. While clustering is a subtype of unsupervised learning, classification, and regression are additional subcategories of supervised learning. This paper discusses the application of clustering using K-means. This methodology explores the concept of lossless picture compression is the process of reducing the size of an image without sacrificing its quality which is achieved using k-mean clustering. The proposed methodology achieves higher compression ratios than other existing approaches to image compression algorithms.