Lossless and near-lossless compression of hyperspectral images based on distributed source coding

This paper addresses the problem of the lossless and near-lossless compression of hyperspectral images and presents two efficient algorithms based on distributed source coding, which perform the lossless compression by means of multilevel scalar codes. The proposed algorithms are implemented on the co-located blocks in the spectral orientation. A novel multiband spectral predictor is proposed to construct the side information of each block. The back-up side information is introduced for the second algorithm to recover the images when the original side information is corrupted by errors. The encoder only requires the transmission of the least significant bit (LSB) bit-planes to the decoder, and the number of bits is computed by the maximum error between the block and its side information. The proposed algorithms are also extended to near-lossless compression. The experimental results show that the proposed algorithms have a competitive compression performance with the existing distributed compression algorithms. Moreover, the proposed algorithms can provide low complexity and different degrees of error resilience, which is suitable for onboard compression.