Matrix Science Mathematic (MSMK)

In light of the remarkable advancements in communication information technology and multimedia internet technology, there arises an increasingly demanding need for the proficient handling of image data pertaining to passenger luggage. This encompasses the critical aspects of image transmission, storage, and compression. The contemporary landscape of passenger figure images presents a formidable data magnitude, which poses considerable trials upon the constraints of limited bandwidth. Consequently, the implementation of digital image compression strives to capture the overarching characteristics of the images utilizing a diminished bit count, while concurrently minimizing the potential degradation during the subsequent image restoration process. The present study incorporates principal component analysis (PCA) in the context of the security inspection industry, specifically focusing on passenger figure images. The application of PCA is integrated within the research framework of image compression and reconstruction systems. In this regard, MATLAB software is utilized to simulate the experiments, followed by an in-depth analysis of the obtained results. The findings demonstrate that by ensuring a cumulative contribution rate of more than 85% from a select few principal components following variable dimension reduction, it is feasible to achieve clear and distortion-free passenger figure images.