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Low complexity block tree coding for hyperspectral image sensors

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Abstract

Complexity of any onboard hyperspectral image sensor is a challenging issue. The existing hyperspectral image compression algorithm plays a great role in reducing the data transmission bandwidth, data processing time, processing power and coding memory. Many wavelet transform-based set partitioned hyperspectral image compression algorithms are proposed in the past which work with lossy and lossless compression. These compression algorithms use lists or state tables to keep track of significant and insignificant sets or coefficients. The 3D wavelet block tree coding (3D-WBTC) has superior coding performance due to the exploitation of the inter sub-band & intra sub-band redundancy. The 3D-Low-Complexity Block Tree Coding (3D-LCBTC) is a novel implementation of 3D-WBTC which uses two state tables and very small size link lists. The 3D-LCBTC uses depth-first search approach which reduces the complexity of the compression process significantly. Thus, the proposed compression algorithm is a suitable candidate for resources-constrained onboard hyperspectral image sensors.

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    Shrish Bajpai

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Bajpai, S. Low complexity block tree coding for hyperspectral image sensors. Multimed Tools Appl 81, 33205–33232 (2022). https://doi.org/10.1007/s11042-022-13057-x

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