Rate Distortion Analysis for Quantization of Distributed Source Coding

2006 ◽  
Author(s):  
Si-ping Wang ◽  
Zhi-wen Liu ◽  
You-gen Xu
Keyword(s):  
Source Coding ◽  
Rate Distortion ◽  
Distributed Source Coding ◽  
Distributed Source ◽  
Distortion Analysis

scholarly journals Low-Complexity Compression Algorithm for Hyperspectral Images Based on Distributed Source Coding

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Yongjian Nian ◽  
Mi He ◽  
Jianwei Wan
Keyword(s):  
Source Coding ◽  
Side Information ◽  
Rate Distortion ◽  
Lossless Compression ◽  
Low Complexity ◽  
Distributed Source Coding ◽  
Compression Algorithm ◽  
Hyperspectral Images ◽  
Multilinear Regression ◽  
Distributed Source

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.

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