Curvelet Transform Based Compression Algorithm for Low Resource Hyperspectral Image Sensors

被引：6

作者：

Bajpai, Shrish ^{[1
]}

Sharma, Divya ^{[2
]}

Alam, Monauwer ^{[3
]}

Chandel, Vishal Singh ^{[4
]}

Pandey, Amit Kumar ^{[4
]}

Tripathi, Suman Lata ^{[5
]}

机构：

[1] Integral Univ, Fac Engn & Informat Technol, Elect & Commun Engn Dept, Lucknow, Uttar Pradesh, India

[2] Inst Engn & Technol, Elect & Commun Engn Dept, Lucknow, Uttar Pradesh, India

[3] Integral Univ, Fac Engn & Informat Technol, Elect Engn Dept, Lucknow, Uttar Pradesh, India

[4] Rajkiya Engn Coll, Appl Sci & Humanities Dept, Ambedkar Nagar, Uttar Pradesh, India

[5] Lovely Profess Univ, Elect & Commun Dept, Kapurthala, Punjab, India

来源：

JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING | 2023年 / 2023卷

关键词：

Coding complexity - Coding gains - Compression algorithms - Curvelet transforms - HyperSpectral - Hyperspectral image compression - Image compression algorithms - Mathematical transforms - Performance - Wavelets transform;

D O I：

10.1155/2023/8961271

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：18

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