An Algorithm for Universal Lossless Compression With Side Information

2006 ◽  
Vol 52 (9) ◽  
pp. 4008-4016 ◽  
Author(s):  
H. Cai ◽  
S.R. Kulkarni ◽  
S. Verdu
Keyword(s):  
Side Information ◽  
Lossless Compression

scholarly journals Sharp Second-Order Pointwise Asymptotics for Lossless Compression with Side Information

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 705
Author(s):  
Lampros Gavalakis ◽  
Ioannis Kontoyiannis
Keyword(s):  
Lower Bound ◽  
Side Information ◽  
Conditional Entropy ◽  
Lossless Compression ◽  
Second Order ◽  
Arbitrary Sequence ◽  
Mixing Conditions ◽  
Compression Algorithms ◽  
Information Density ◽  
Conditional Information

The problem of determining the best achievable performance of arbitrary lossless compression algorithms is examined, when correlated side information is available at both the encoder and decoder. For arbitrary source-side information pairs, the conditional information density is shown to provide a sharp asymptotic lower bound for the description lengths achieved by an arbitrary sequence of compressors. This implies that for ergodic source-side information pairs, the conditional entropy rate is the best achievable asymptotic lower bound to the rate, not just in expectation but with probability one. Under appropriate mixing conditions, a central limit theorem and a law of the iterated logarithm are proved, describing the inevitable fluctuations of the second-order asymptotically best possible rate. An idealised version of Lempel-Ziv coding with side information is shown to be universally first- and second-order asymptotically optimal, under the same conditions. These results are in part based on a new almost-sure invariance principle for the conditional information density, which may be of independent interest.

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.

Channel Coding in the Presence of Side Information

2008 ◽  
Author(s):  
Guy Keshet ◽  
Yossef Steinberg ◽  
Neri Merhav
Keyword(s):  
Channel Coding ◽  
Side Information

PTS-Based PAPR Reduction by Iterative p-Norm Minimization without Side Information in OFDM Systems

2018 ◽  
Vol E101.B (3) ◽  
pp. 856-864 ◽  
Author(s):  
Moeko YOSHIDA ◽  
Hiromichi NASHIMOTO ◽  
Teruyuki MIYAJIMA
Keyword(s):  
Side Information ◽  
Papr Reduction ◽  
Ofdm Systems ◽  
Norm Minimization
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