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A low complexity hyperspectral image compression through 3D set partitioned embedded zero block coding

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Abstract

Memory management of the hyperspectral image sensor is a challenging issue. The existing hyperspectral image compression schemes play a dominant role in minimizing the cost of storage equipment and bandwidth for data transmission for resource constraints onboard hyperspectral image sensors. Traditionally many transform-based set partition hyperspectral image compression algorithms are proposed, but these compression schemes use the data-dependent link list to keep track of the significant or insignificant coefficients or block cube sets, and the size of the lists increases swiftly with the encoding rate. The data-dependent list management and multiple memory read or write operations slow down the compression scheme. Many attempts had been made to address the memory issue through the replacement of the dynamic lists by the static fixed size state tables. The memory required for the state table depends upon the dimension of the hyperspectral image and at the low bit rates, it requires a lot of memory. This paper presents the novel hyperspectral image compression scheme for the hyperspectral image sensor that eliminates the linked list and state table. The obtained experimental results show that the proposed compression scheme outperforms state of the art transform hyperspectral image compression schemes in terms of coding memory and computation complexity while maintaining the coding efficiency. Due to the low complex nature, the proposed scheme saves the operation time and energy for the coding operation. The proposed compression scheme is a suitable candidate for the lossy data transmission and for the low memory hyperspectral sensors.

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Acknowledgements

We are sincerely thankful to the anonymous reviewers for their critical comments and suggestions to improve the quality of the paper.

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Authors and Affiliations

  1. Electronics Engineering Department, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India

    Shrish Bajpai

  2. Department of ECE, Faculty of Engineering, Integral University, Lucknow, Uttar Pradesh, India

    Naimur Rahman Kidwai

  3. Electronics Engineering Department, K.N.I.T., Sultanpur, Uttar Pradesh, India

    Harsh Vikram Singh

  4. Department of CSE, NIT Patna, Patna, Bihar, India

    Amit Kumar Singh

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Bajpai, S., Kidwai, N.R., Singh, H.V. et al. A low complexity hyperspectral image compression through 3D set partitioned embedded zero block coding. Multimed Tools Appl 81, 841–872 (2022). https://doi.org/10.1007/s11042-021-11456-0

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