Analysis of lossy to near-lossless compression of hyperspectral imagery using prediction and multi-stage vector quantisation

摘要

Compression of hyperspectral images on-board is being pursued diligently for the past decade due to the exhaustive applications in the arena of remote sensing. Compression algorithms have to be designed in the interest of using minimum number of bits to represent a pixel at the cost of maintaining the quality of the reconstructed signal. Referring to the above context, compression algorithm has been proposed based on multi-stage vector quantisation affirming a bit rate as low as possible, meanwhile preserving the quality of the signal. The spectral redundancy is well exploited in the pre-processing stage, the occurrence of which aids the functioning of vector quantisation, resulting in the lower bit rate in the subsequent stages of decorrelation. The proposed algorithm reforms the vector quantisation in terms of spectral distortion, complexity and memory requirements. The proposed strategy divulges with a peak signal to noise ratio of 82.55 dB and signal to distortion noise ratio of 92.23 dB.