Segmented Fractional Wavelet Filter Based Low Memory Hyperspectral Image Coding Algorithm for Wireless Multimedia Sensor Networks
| International Journal of Electronics and Communication Engineering |
| © 2025 by SSRG - IJECE Journal |
| Volume 12 Issue 6 |
| Year of Publication : 2025 |
| Authors : Rajesh, Shrish Bajpai, Naimur Rahman Kidwai |
10.14445/23488549/IJECE-V12I6P106
How to Cite?
Rajesh, Shrish Bajpai, Naimur Rahman Kidwai, "Segmented Fractional Wavelet Filter Based Low Memory Hyperspectral Image Coding Algorithm for Wireless Multimedia Sensor Networks," SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 6, pp. 65-89, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I6P106
Abstract:
Recently, the rapid development of transform-based coding has improved the performance of hyperspectral image sensors. Each hyperspectral image has a size of more than a hundred MB, which needs to be compressed before the transmission to save the data transmission bandwidth, reduce the sensor complexity, increase the sensor performance, and lower the energy consumption. This work proposed a novel lossy hyperspectral image compression algorithm, which has high coding efficiency, decreases coding complexity and reduces overall memory (coding and transform) demand. The Segmented Fractional Wavelet Filter (SFrWF) is a low-memory mathematical transform approach to compute the transform coefficient of the hyperspectral image. The Low Complexity Zero Memory Set Partitioned Embedded bloCK (LC-ZM-SPECK) is employed to code coefficients of the transform hyperspectral image, which is applied on a frame-by-frame basis. The simulation result shows that the proposed compression algorithm is faster than other state-of-the-art compression algorithms, making it an appropriate choice for low hardware resource hyperspectral image sensors.
Keywords:
Sensor performance, Transform memory, Transform coding, Hyperspectral image compression, Set partitioned algorithm.
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