Curvelet Transform Based Compression Algorithm for Low Resource Hyperspectral Image Sensors. (20th February 2023)

• Record Type: Journal Article
• Authors: Bajpai, Shrish; Sharma, Divya; Alam, Monauwer; Chandel, Vishal Singh; Pandey, Amit Kumar; Tripathi, Suman Lata
• Other Names: Ramash Kumar K. Academic Editor.
• Is Part Of: Journal of electrical and computer engineering. Volume 2023(2023)
• Publication Date: 2023-02-20
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; 621.3905
• DOI: 10.1155/2023/8961271
• Languages: English
• ISSNs: 2090-0147

Abstract

Abstract : The wavelet transform is widely used in the task of hyperspectral image compression (HSIC). They have achieved outstanding performance in the compression of a hyperspectral (HS) image, which has attracted great interest. However, transform based hyperspectral image compression algorithm (HSICA) has low-coding gain than the other state of art HSIC algorithms. To solve this problem, this manuscript proposes a curvelet transform based HSIC algorithm. The curvelet transform is a multiscale mathematical transform that represents the curve and edges of the HS image more efficiently than the wavelet transform. The experiment results show that the proposed compression algorithm has high-coding gain, low-coding complexity, at par coding memory requirement, and works for both (lossy and lossless) compression. Thus, it is a suitable contender for the compression process in the HS image sensors.