Compression of Large-Scale Image Dataset using Principal Component Analysis and K-means Clustering

Digital images, being on the verge of its utmost popularity encompasses plenty of applications and as such are generated at an unprecedented rate. These digital form of data are often found with redundant information. Applications that require a bulk amount of images to be processed, turn out to be high regarding computational complexity. Needless to say, it leads to inefficient storage utilization. In this paper, a hybrid approach is applied to compress a large-scale image data-set by combining two popular algorithms: Principal Component Analysis (PCA) and K-means. This paper works with a view to diminishing the redundant information by implementing dimensionality reduction followed by color quantization. The PCA is used to project the data onto a lower dimensional space with retaining as maximum variance as possible. The K-means algorithm is used to restrict the distinct number of colors to represent an image by means of clustering the data together. The results obtained from the proposed method is compared with the results obtained from implementing PCA and K-means clustering algorithms independently, where the proposed method provides with a better compression ratio.