Highly Scalable Compression Method for Super Resolution Multi Spectral Images

Abstract

This paper provides a new technique for lossy multispectral images compression at very high data rates. Image compression is becoming more and more important, as new multispectral instruments are going to generate very high data rates due to the increased spatial and spectral resolutions. The Lossless compression does not provide a sufficient degree of data volume reduction to meet the bandwidth requirements of the downlink channel, so a lossy approach is the only possible solution. Multispectral data exhibits a large spectral correlation. Many techniques have been proposed for multispectral image data. Mainly in all the compression techniques to minimize the intra-band and inter-band correlation, some of them do in a single step and others do in a two-step transformation followed by an encoding scheme to eliminate the statistical redundancy or to give a desired bit-rate at low compression ratios based on the wavelet transform, where the spatial and the spectral redundancy are removed simultaneously. At higher compression ratios, a high quality wavelet based method in the spatial dimensions combined with the Improved Set Partitioning Hierarchical Trees (ISPHIT) in the spectral dimension gives the best results.