3D Single List Set Partitioning in Hierarchical Trees For Onboard Hyperspectral Image Sensors

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 4

Year of Publication : 2025

Authors : Vinod Kumar Tripathi, Shrish Bajpai

10.14445/23488549/IJECE-V12I4P125

How to Cite?

Vinod Kumar Tripathi, Shrish Bajpai, "3D Single List Set Partitioning in Hierarchical Trees For Onboard Hyperspectral Image Sensors," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 4, pp. 265-281, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I4P125

Abstract:

The 3D-Set Partitioning in Hierarchical Trees (3D-SPIHT) has a moderate coding complexity and average coding memory requirement. It provides an embedded compressed bit stream that can be easily decoded at different data rates. These characteristics contribute to the algorithm's overall attractiveness. Unfortunately, due to the fact that it uses three linked lists to record the significance status of the coefficients, it requires an extremely large amount of computation memory. The random access to these lists also results in the 3D-SPIHT having a memory management system that is somewhat complicated. This manuscript presents a new compression algorithm called 3D-Single List SPIHT (3D-SLS). The memory requirements for the proposed 3D-SLS HSICA are extremely low since it requires around several folds lower memory requirement than the original 3D-SPIHT required. This is accomplished by using a single list in conjunction with two state mark bitmaps, as opposed to the three lists that 3D-SPIHT have. In addition, the proposed 3D-SLS offers a simpler memory management system because coefficients are never deleted from the list after they have been added to the list. In addition, the list size can be predetermined, which allows one to circumvent the issue of dynamic memory allocation. Because of these memory savings and management simplifications, the 3D-SLS compression algorithm is an excellent candidate for implementation in hardware for the onboard hyperspectral image sensors.

Keywords:

Lossy hyperspectral image compression, Wavelet transform coding, Zero tree, Set Partitioned Compression Algorithm.

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