Implementation of image compression based on singular value decomposition
Global Journal of Engineering and Technology Advances, 11 (3):
086–092 (June 2022)DOI: 10.30574/gjeta.2022.11.3.0097
Abstract
Analyzing big data amount and the limitation of the storage data devices and the rate of data is one of the most critical issues in data processing and transferring. In this research, one of the essential approaches for image compression is proposed. Singular value decomposition (SVD) is a highly effective mathematic technique which is used for the reduction process applied to redundant data in order to minimize the required storage space or transferring channel. The main idea of this work is divided into two main phases. The first phase is explained the (SVD) computational steps approach in detailed while the second phase is described the result of the applying (SVD) in the field of image compression. The achievement results of this experiment show a powerful controlled technique depend on the desired rank of a decomposed image in order to achieve a compression result by the using of a factorized matrix on image compression. The performance of the compressed image is examined in terms of peak to signal ratio and compression ratio.