We address the problem of the lossless compression of hyperspectral images and present two efficient algorithms inspired by the distributed source coding principle, which perform the compression by means of the blocked coset coding. In order to make full use of the intraband and interband correlation, the prediction error block scheme and the multiband prediction scheme are introduced in the proposed algorithms. In the proposed algorithms, the prediction error of each block is partitioned into prediction error blocks of size . The bit rate of the pixels corresponding to the prediction error block is determined by its maximum prediction error. This processing takes advantage of the local correlation to reduce the bit rate efficiently and brings the negligible increase of additional information. In addition to that, the proposed algorithms can be easily parallelized by having different blocks compressed at the same time. Their performances are evaluated on AVIRIS images and compared with several existing algorithms. The experimental results on hyperspectral images show that the proposed algorithms have a competitive compression performance with existing distributed compression algorithms. Moreover, the proposed algorithms can provide low-codec complexity and high parallelism, which are suitable for onboard compression.