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http://192.168.9.248:8080/jspui/handle/123456789/807
Title: | 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT) |
Authors: | Chandra, Harshit Bajpai, Shrish |
Keywords: | Electronics & Communication Engineering |
Issue Date: | 2022 |
Publisher: | IEEE |
Abstract: | Hyperspectral (HS) image has rich spectral information content, which facilitates multiple applications including remote sensing. Due to the big data size of the HS image, compression is a required process for the efficiency of image storage and transmission. However, the complexity of the compression algorithms turns real-time compression into a very challenging task. A novel listless set partitioned hyperspectral image compression algorithm is proposed. The proposed compression algorithm uses zero block cube tree structure to exploit the inter and intra sub-band correlation to achieve the compression. From the result, it has been clear that the proposed compression algorithm has low coding complexity with at-par coding efficiency. Thus, it can be a suitable contender for low-resource hyperspectral image sensors. |
Description: | Listless Block Cube Tree Coding for Low Resource Hyperspectral Image Compression Sensors by Harshit Chandra, Shrish Bajpai |
URI: | https://ieeexplore.ieee.org/abstract/document/10029076 http://192.168.9.248:8080/jspui/handle/123456789/807 |
ISBN: | 978-1-6654-7647-8 |
Appears in Collections: | Books/Book Chapters/Edited Books |
Files in This Item:
File | Description | Size | Format | |
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Listless Block Cube Tree Coding for Low Resource Hyperspectral Image Compression Sensors - Copy.pdf | IEEE | 858.18 kB | Adobe PDF | View/Open |
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