Lossless Compression of Correlated Images/Data with Low Complexity Encoder Using Distributed Source Coding Techniques

Author(s):  
Mortuza Ali ◽  
Manzur Murshed
2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Yongjian Nian ◽  
Mi He ◽  
Jianwei Wan

A low-complexity compression algorithm for hyperspectral images based on distributed source coding (DSC) is proposed in this paper. The proposed distributed compression algorithm can realize both lossless and lossy compression, which is implemented by performing scalar quantization strategy on the original hyperspectral images followed by distributed lossless compression. Multilinear regression model is introduced for distributed lossless compression in order to improve the quality of side information. Optimal quantized step is determined according to the restriction of the correct DSC decoding, which makes the proposed algorithm achieve near lossless compression. Moreover, an effective rate distortion algorithm is introduced for the proposed algorithm to achieve low bit rate. Experimental results show that the compression performance of the proposed algorithm is competitive with that of the state-of-the-art compression algorithms for hyperspectral images.


2005 ◽  
Author(s):  
David Papini ◽  
Mauro Barni ◽  
Andrea Abrardo ◽  
Andrea Garzelli ◽  
Enrico Magli

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