Principal component analysis to reduce dimension on digital image

High-resolution image is referred as high-dimensional data space as each image data is organized into two-dimensional pixel values in which each pixel consists of its respective RGB bits value. The representation of image data poses a challenge to sharing image files over Internet. The lengthy image uploading and downloading time has always been a major issue for Internet users. Apart from data transmission problem, high-resolution image consumes greater storage space. Principal Component Analysis (PCA) is a mathematical technique to reduce the dimensionality of data. It works on the principal of factoring matrices to extract the principal pattern of a linear system. This paper aims to evaluate the application of PCA on digital image feature reduction and compare the quality of the feature reduced images with difference variance values. As a result of summarizing the preliminary literature, dimension reduction process by PCA generally consists of four major steps: (1) normalize image data (2) calculate covariance matrix from the image data (3) perform Single Value Decomposition (SVD) (4) find the projection of image data to the new basis with reduced features. Experimental results showed that PCA technique effectively reduces the dimension of image data while still maintaining the principal properties of the original image. This technique achieved 35.3% for the file size reduction for the best feature reduced quality. The transmission time of image file over Internet has achieved significant improvement especially for the download activity via mobile devices.