A Comparative Study On Image Compression in Cloud Computing

Cloud computing is a computing model that offers integrated ICT infrastructure, for example, virtual resources, including processing activities, storage, software, and services, accessible through an Internet network. These resources are distributed and can be accessed from any device and location as needed. The use of cloud computing has been increasing, particularly for image storage, to alleviate the storage burden on physical media. Therefore, this research aims to compare different image compression techniques, namely Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and Karhunen-Loève Transform (KLT), within the context of cloud computing.The study involves comparing the effectiveness of these four methods based on the level of compression achieved, processing speed during compression, and the similarity between the compressed and original images. Six sample images are utilized for testing purposes. The results indicate that the DWT method outperforms the SVD, KLT, and DCT methods in terms of file compression size, achieving an average compression rate of 12% compared to the original file. Additionally, the DWT method exhibits an average compression speed of 4.31 seconds and a grayscale similarity score of 0.96 with the original image. However, in terms of color scale similarity, the SVD method achieves a higher score of 13.1 compared to the DWT method.