Block-Based Fractional Wavelet Filter For Low-Complexity Coding Algorithm of Hyperspectral Image With Memory Constrained Wireless Multimedia Sensor

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 8

Year of Publication : 2025

Authors : Rajesh, Shrish Bajpai, Naimur Rahman Kidwai

10.14445/23488549/IJECE-V12I8P123

How to Cite?

Rajesh, Shrish Bajpai, Naimur Rahman Kidwai, "Block-Based Fractional Wavelet Filter For Low-Complexity Coding Algorithm of Hyperspectral Image With Memory Constrained Wireless Multimedia Sensor," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 8, pp. 257-271, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I8P123

Abstract:

Depending on the mammoth size of the hyperspectral image, the wavelet transform-based compression algorithm has achieved impressive performance in the compression of hyperspectral images. Set partition wavelet transform hyperspectral image compression algorithms have superior performance than other transform compression algorithms, such as embeddedness, low coding complexity and high coding efficiency. Fractional wavelet-based zero memory set partitioned embedded block (ZM-SPECK) reduces the demand for transform memory and coding memory with at par transform complexity and high coding efficiency. But comparing every coefficient/block/set for each frequency frame with the current threshold is time-consuming. The present algorithm deals with the complexity and memory of the transform image coding algorithms. The block-based fractional wavelet filter delivers exact transform results like other wavelet transforms, but demands the least transform memory with at par wavelet transform complexity. With the employment of the low complexity zero memory set partitioned embedded block (LC-ZM-SPECK), the coding complexity of the compression algorithm is further reduced. The simulation results show that the proposed compression algorithm reduces the overall complexity by ~ 25% to other state-of-the-art compression algorithms and reduces the transform memory by ~ 40%, making it a suitable choice for the resource-constrained hyperspectral image sensors.

Keywords:

Hyperspectral image analysis, Coding algorithm, Wavelet transform, Fractional wavelet filter, Wireless sensor network.

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