Hyperspectral image compression based on simultaneous sparse representation and general-pixels

Simultaneous sparse representation can transform the correlated spectral signatures of hyperspectral pixel matrixes into sparse coefficients. It can be very efficient in the compression scheme when the original image is clustered to general-pixels (a cluster of hyperspectral pixels which contains the similar signature). In this paper, we propose a simultaneous sparse representation based hyperspectral image compression scheme. First, the whole hyperspectral pixels are clustered into general-pixels and each general-pixel will be coded by the simultaneous sparse representation scheme. To further compress the coefficients, the differential pulse code modulation filter is adopted in each row coefficients. Finally, all the nonzero coefficients, over-complete dictionary and mapping data of general-pixels will be transformed into the binary bitstream by Huffman coding. The results on four hyperspectral image datasets show that our method outperforms several classical and the state-of-the-art methods in term of rate-distortion and spectral fidelity performance.