We present a simple and e cient scheme for using the Set Partitioning in Hierarchical Trees (SPIHT) image compression algorithm 1] in a generalized multiple description framework. To combat packet loss, controlled amounts of redundancy are added to the original data during the compression process. Unequal loss protection is implemented by varying the amount of redundancy with the importance of data. The algorithm achieves graceful degradation of image quality in the presence of increasing description loss; high image quality is obtained even when over half of the descriptions are lost.

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We present an algorithm for lossy compression of hyperspectral images for implementation on field programmable gate arrays (FPGA). To greatly reduce the bit rate required to code images, we use linear prediction between the bands to exploit the large amount of inter-band correlation. The prediction residual is compressed using the Set Partitioning in Hierarchical Trees algorithm. To reduce the complexity of the predictive encoder, we propose a bit plane-synchronized closed loop predictor that does not require full decompression of a previous band at the encoder. The new technique achieves almost the same compression ratio as standard closed loop predictive coding and has a simpler on-board implementation.

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Algorithms for lossless and lossy compression of hyperspectral images are presented. To greatly reduce the bit rate required to code images and to exploit the large amount of inter-band correlation, linear prediction between the bands is used. Each band, except the first one, is predicted by previously transmitted band. Once the prediction is formed, it is subtracted from the original * This work appeared in part in the