Compression of Ultra High Definition Image based on K-means Clustering Algorithm

Image compression plays a key role in contemporary digital environments given the escalating influx of visual data on the Internet. In this study, the K-means clustering algorithm is adopted for Ultra High Definition (UHD) image compression. The procedures are introduced in detail, and a numerical example is provided to demonstrate the applicability of the proposed approach. The results indicate that with an appropriately assigned K value, that is, the number of clusters, the K-means can strike a good balance between the visual quality loss and file size reduction, achieving a compression ratio of 60.37. This study not only proposes an approach for UHD image compression, but also exhibits the promising potential of clustering algorithms in the field of image processing.