i. The KL Transform is based on the statistical properties of the image and has several important properties that make it useful for image processing particularly for image compression.

ii. The main purpose of image compression is to store the image in fewer bits as compared to original image, now data from neighboring pixels in an image are highly correlated.

iii. More image compression can be achieved by de-correlating this data. The KL transform does the task of de-correlating the data thus facilitating higher degree of compression

iv. Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly de-correlated variables called principal components.

v. Now, both KL Transform and PCA are used for de-correlating the data, the version of KL transform where the coefficients for KL transform are computed from a sample, is known as principal component analysis (PCA)

Thus KL Transform is also known as PCA.