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Objective With the rapid development of spectral imaging technology in recent years, hyperspectral remote sensing images can provide abundant data on surface features. However, the sizable data of hyperspectral images make their storage, transmittal, and application quite difficult. As a result, how to validly code hyperspectral images has become a hot issue. Method The distributed source coding based on coset codes has received much attention because of its good compression performance and low coding complexity. In this study, we present a scheme for lossless distributed source coding of hyperspectral images based on adaptive quadtree segmentation. Assuming that the first frame in every group of spectrum frames is the key frame, the other frames are Wyner-Ziv frames. First, we perform adaptive quadtree segmentation on the key frame and then optimum linear prediction on each block of each Wyner-Ziv frame. Afterwards, the index of the coset codes to be transferred and the k least significant bits of every pixel in this block are ascertained using prediction residuals. Result In this study, adaptive quadtree segmentation scheme has been proved to strengthen the adaptability of the formed coset codes. The proposed scheme can achieve a good compromise between coding efficiency and calculating complexity. Conclusion This scheme is better able to meet the lossless coding requirement for hyperspectral images under low-complexity environment. [ABSTRACT FROM AUTHOR]

The article presents a study which offers encoding schemes for distributed compressive video sampling (DCVS) and provides comparison with the channel coding based distributed video coding (DVC).