TY - GEN
T1 - A Comparative Study On Image Compression in Cloud Computing
AU - Nugroho, Tri Yulianto
AU - Nur Hidayat, Ahmad
AU - Filsafan, Mas Syahdan
AU - Arif Ardiansyah, Yoehan
AU - Santoso, Bagus Jati
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - DCT
KW - DWT
KW - ICT infrastructure
KW - KLT
KW - SVD
KW - cloud computing
KW - image compression
UR - http://www.scopus.com/inward/record.url?scp=85178026571&partnerID=8YFLogxK
U2 - 10.1109/EECSI59885.2023.10295602
DO - 10.1109/EECSI59885.2023.10295602
M3 - Conference contribution
AN - SCOPUS:85178026571
T3 - International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
SP - 219
EP - 225
BT - Proceeding - EECSI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2023
Y2 - 20 September 2023 through 21 September 2023
ER -