This paper presents a novel dynamically reconfigurable hardware architecture for lossless compression and its optimization for space imagery. The proposed system makes use of reconfiguration to support optimal modeling strategies adaptively for data with different dimensions. The advantage of the proposed system is the efficient combination of different compression functions. For image data, we propose a new multi-mode image model which can detect the local features of the image and use different modes to encode regions with different features. Experimental results show that our system improves compression ratios of space image while maintaining low complexity and high throughput. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Chen, X., Canagarajah, C. N., Vitulli, R., & Nunez-Yanez, J. L. (2008). Lossless compression for space imagery in a dynamically reconfigurable architecture. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4943 LNCS, pp. 336–341). https://doi.org/10.1007/978-3-540-78610-8_38
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