Abstract:
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.
