Affiliations: [a] Department of Electronics & Communication Engineering, Faculty of Engineering & Information Technology, Integral University, Lucknow, Uttar Pradesh, India
| [b] Department of Electrical Engineering, Faculty of Engineering & Information Technology, Integral University, Lucknow, Uttar Pradesh, India
| [c] Department of Applied Science and Humanities, Rajkiya Engineering College, Ambedkar Nagar, Uttar Pradesh, India
| [d] Department of Technical Education, Institute of Engineering and Technology, Dr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India
Correspondence:
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Corresponding author. Shrish Bajpai, Department of Electronics & Communication Engineering, Faculty of Engineering & Information Technology, Integral University, Lucknow, Uttar Pradesh, India. E-mail: shrish@iul.ac.in.
Abstract: Hyperspectral (HS) images contain rich spatial and spectral information. Due to its large size, it is difficult to store, process, analyze, or transmit the critical information contained in it. The compression of hyperspectral images is inevitable. Many transform based Hyper Spectral Image Compression Algorithms (HSICAs) have been proposed in the past that work for both lossy and lossless compression processes. The transform based HSICA uses linked lists or dedicated markers or array structure to keep track of significant and insignificant sets or coefficients of a transformed HS image. However, these algorithms either suffered from low coding efficiency, high memory requirements, or high coding complexity. This work proposes a transform based HSICA using a curvelet transform to improve the directional elements and the ability to represent edges and other singularities along curves. The proposed HSICA aims to provide superior quality compressed HS images by representing HS images at different scales and directions and to achieve a high compression ratio. Experimental results show that the proposed algorithm has a low coding memory requirement with a 2% to 5% increase in coding gain compared to the other state of art compression algorithms.
