Distributed lossless coding of hyperspectral images
In this paper we propose a novel distributed lossless compression scheme for hyperspectral images. All the images/bands are encoded independently, and the spectral correlation is exploited using distributed coding technologies in order to achieve low encoding complexity. At the encoder, sub-sampled images are successively encoded and transmitted. At the decoder, side information is generated with the knowledge of decoded sub-sampled images and other previously decoded bands. Reference bands are adaptively selected, and sliding window prediction or k nearest neighbor prediction is performed to capture the spatially varying spectral characteristics. Experimental results on AVRIS data demonstrate that the proposed scheme achieves competitive compression performance with respect to other state-of-the-art 3D codecs and with even lower encoding complexity than 2D codecs.