Block-Based Fractional Wavelet Filter for Compression of Hyperspectral Images over Wireless Multimedia Sensor Network Platforms

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 3

Year of Publication : 2025

Authors : Rajesh, Shrish Bajpai, Naimur Rahman Kidwai

10.14445/23488549/IJECE-V12I3P103

How to Cite?

Rajesh, Shrish Bajpai, Naimur Rahman Kidwai, "Block-Based Fractional Wavelet Filter for Compression of Hyperspectral Images over Wireless Multimedia Sensor Network Platforms," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 3, pp. 21-42, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I3P103

Abstract:

With the property of excellent energy compaction and the ability to analyse hyperspectral images in the space-frequency domain, the 3D Dyadic Wavelet Transform is broadly used for the compression of hyperspectral images. However, calculating the transform coefficient through the conventional wavelet transform is memory intensive, which makes the compression algorithm unsuitable for the resource constraint hyperspectral image sensors. In the present study, the transform coefficients are calculated through block-based fractional wavelet transform frame by frame of the hyperspectral image to reduce the transform memory. The transform coefficient is coded with the zero memory set partitioned embedded block. We evaluated the proposed compression algorithm through MATLAB simulations with six popular hyperspectral images. From the result, it has been observed that demand for transform memory and transform complexity is reduced significantly while the requirement of the coding memory is zero and coding complexity is at par with other listless compression algorithms. Thus, the proposed compression algorithm is an optimum choice for the resource constraint hyperspectral image sensors.

Keywords:

Hyperspectral Image Compression, Low Memory Architecture, Lossy Compression, Hyperspectral Image Coding, Wavelet Transform, Low Complexity.

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