Abstract
Color images usually have large storage sizes as they are composed of three planes in the raw image format represented with the red, green, and blue components. Traditional color image compression schemes can be used to save the storage size of the color images. Unfortunately, most of these schemes are lossy in nature, which affects the details of color images. This paper presents a different treatment to the color image storage problem depending on the original color image formation process. In the color image formation process, not all the red, green, and blue components of the color images are acquired, simultaneously. Only, one component at each pixel position is acquired and Color Filter Array (CFA) interpolation is used to estimate the other two components using interpolation algorithms like Minimized-Laplacian Residual Interpolation (MLRI) and Linear Interpolation with Laplacian Second Order Correction (LILSOC). We adopt a similar strategy in this paper for reducing the storage sizes of color images by 66.67% of their original sizes. The sensitivity of the pattern recognition process to the proposed color image storage and interpolation strategy is studied in this paper. A cepstral feature extraction algorithm is adopted in this paper for extracting features from the interpolated patterns for further classification. Moreover, two types of classifiers are considered and compared in this paper for the pattern recognition: Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs). Simulation results reveal the success of the proposed strategy for color image storage and interpolation in obtaining high-quality color images in addition to the high Recognition Rates (RR) of color patterns after interpolation. This success encourages the use of the proposed color image storage strategy in storing large volumes of color databases.