A hyperspectral image compression algorithm of maximum compression error controllable

A novel algorithm for hyperspectral image compression is proposed in order to control compression error in the process of real-time image compression. In the first step, Singular Value Decomposition (SVD) of the original image matrix was computed and the singular values and singular vectors of the matrix were obtained; the image was then reconstructed with a smaller set of singular values and singular vectors. In the second step, the spectrum errors between the regional image and reconstructed image were calculated by subtracting the reconstructed image spectrum from the original image spectrum; the quantification for the spectrum errors could be obtained by dividing the maximum spectrum errors got from the first step with the acceptable error of the test system. Lastly, the singulars values and singular vectors for reconstructing image were compressed by lossless predictive coding and arithmetic coding, the quantified spectrum errors were also compressed by a novel lossless compression algorithm of non-zero element coding designed in this paper. The results of the simulation on the hyperspectral images of Luna Lake and Low Altitude show that when the maximum relative errors are controlled to be 0.44% and 0.33% respectively, the compression ratios are 41.5:1 and 24.6:1, the SNRs are 50.4 dB and 47.8 dB.