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3D-listless block cube set-partitioning coding for resource constraint hyperspectral image sensors

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Abstract

The hyperspectral image provides rich spectral information content, which facilitates multiple applications. With the rapid advancement of the spatial and spectral resolution of optical instruments, the image data size has increased by many folds. For that, it requires a compression algorithm having low coding complexity, low coding memory demand and high coding efficiency. In recent years, many coding algorithms are proposed. The wavelet transform-based set-partitioned hyperspectral compression algorithms have superior coding performance. These algorithms employ linked lists or state tables to track the significant/insignificant of the partitioned sets/coefficients. The proposed algorithm uses the pyramid hierarchy property of wavelet transform. The markers are used to track the significance/insignificance of the pyramid level. A single pyramid level has many sets. An insignificant pyramid level having multiple sets is represented as a single bit in proposed compression algorithm, while a single insignificant set in 3D Set Partition Embedded bloCK (3D-SPECK) and 3D-Listless SPECK (3D-LSK) is represented as a single bit. Through this, the requirement of the bits in the proposed algorithm is less than other wavelet transform compression algorithms at the high bit planes. The simulation result shows that the proposed compression algorithm has high coding efficiency with very less coding complexity and moderate coding memory requirement. The reduced coding complexity improves the performance of the image sensor and lowers the power consumption. Thus, the proposed compression algorithm has great potential in low-resource onboard hyperspectral imaging systems.

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I am sincerely thankful to the anonymous reviewers for their critical comments and suggestions to improve the quality of the paper. The MCN for this manuscript is IU/R&D/2023-MCN002303.

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    Shrish Bajpai

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Bajpai, S. 3D-listless block cube set-partitioning coding for resource constraint hyperspectral image sensors. SIViP 18, 3163–3178 (2024). https://doi.org/10.1007/s11760-023-02979-0

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