Volume 68, Issue 1, August 2023, Pages 84–97
Livane NGUEJIO ZEBAZE1, ERNEST KIATA2, and Laurent BITJOKA3
1 Université de Ngaoundéré, Laboratoire de Physique Appliquée (PAP), Cameroon
2 Départment de Physiques Faculté des Sciences (FS) de l’Université de Ngaoundéré, Cameroon
3 Université de Ngaoundéré, Laboratoire Energie, Signal, Imagerie et Automatique (LESIA), Cameroon
Original language: English
Copyright © 2023 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Image compression is a process of reducing the number of bits needed to represent an image. The goal is to optimize storage spaces, facilitate their transmission through the network and thus promote telemedicine. Over the years, several compression algorithms have distinguished themselves by their ability to reduce the size of the image while maintaining an acceptable visual appearance. These include the JPEG standard, the JPEG2000 standard and many others. The principle of these algorithms is essentially based on the reduction wavelet coefficients according to the singularity of the image. In this article, a new approach is proposed. The goal of this approach is to zero the wavelet coefficients regardless of the singularity of the image. To achieve this goal, our algorithm segments into three fundamental parts. The first part consists in breaking down the image into sub-bands through the QWT formalism. Subsequently, in order to obtain orthogonal matrices, we break down the matrices of the recently obtained sub-bands into singular values. The objective of these matrices is to exploit the redundancy present in the image while putting most wavelet coefficients to zero without, however significantly degrading the visual aspect of the image. To close the algorithm, we apply a thresholding function to the previously obtained wavelet coefficients. The method was evaluated by computer performance criteria such asand by human visual system performance criteria such as. These criteria are used to judge the quality of the reconstructed image and the compression ratio.
Author Keywords: Discrete wavelet transform, Medical image, Quaternionic wavelet transform, Image compression, Orthogonal basis, Multiresolution analysis.
Livane NGUEJIO ZEBAZE1, ERNEST KIATA2, and Laurent BITJOKA3
1 Université de Ngaoundéré, Laboratoire de Physique Appliquée (PAP), Cameroon
2 Départment de Physiques Faculté des Sciences (FS) de l’Université de Ngaoundéré, Cameroon
3 Université de Ngaoundéré, Laboratoire Energie, Signal, Imagerie et Automatique (LESIA), Cameroon
Original language: English
Copyright © 2023 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Image compression is a process of reducing the number of bits needed to represent an image. The goal is to optimize storage spaces, facilitate their transmission through the network and thus promote telemedicine. Over the years, several compression algorithms have distinguished themselves by their ability to reduce the size of the image while maintaining an acceptable visual appearance. These include the JPEG standard, the JPEG2000 standard and many others. The principle of these algorithms is essentially based on the reduction wavelet coefficients according to the singularity of the image. In this article, a new approach is proposed. The goal of this approach is to zero the wavelet coefficients regardless of the singularity of the image. To achieve this goal, our algorithm segments into three fundamental parts. The first part consists in breaking down the image into sub-bands through the QWT formalism. Subsequently, in order to obtain orthogonal matrices, we break down the matrices of the recently obtained sub-bands into singular values. The objective of these matrices is to exploit the redundancy present in the image while putting most wavelet coefficients to zero without, however significantly degrading the visual aspect of the image. To close the algorithm, we apply a thresholding function to the previously obtained wavelet coefficients. The method was evaluated by computer performance criteria such asand by human visual system performance criteria such as. These criteria are used to judge the quality of the reconstructed image and the compression ratio.
Author Keywords: Discrete wavelet transform, Medical image, Quaternionic wavelet transform, Image compression, Orthogonal basis, Multiresolution analysis.
How to Cite this Article
Livane NGUEJIO ZEBAZE, ERNEST KIATA, and Laurent BITJOKA, “Development of a new medical image compression method for optimal storage space,” International Journal of Innovation and Scientific Research, vol. 68, no. 1, pp. 84–97, August 2023.
