scholarly journals Source Coding with a Causal Helper

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 553
Author(s):  
Shraga I. Bross

A multi-terminal network, in which an encoder is assisted by a side-information-aided helper, describes a memoryless identically distributed source to a receiver, is considered. The encoder provides a non-causal one-shot description of the source to both the helper and the receiver. The helper, which has access to causal side-information, describes the source to the receiver sequentially by sending a sequence of causal descriptions depending on the message conveyed by the encoder and the side-information subsequence it has observed so far. The receiver reconstructs the source causally by producing on each time unit an estimate of the current source symbol based on what it has received so far. Given a reconstruction fidelity measure and a maximal allowed distortion, we derive the rates-distortion region for this setting and express it in terms of an auxiliary random variable. When the source and side-information are drawn from an independent identically distributed Gaussian law and the fidelity measure is the squared-error distortion we show that for the evaluation of the rates-distortion region it suffices to choose the auxiliary random variable to be jointly Gaussian with the source and side-information pair.

2010 ◽  
Vol 4 (18) ◽  
pp. 2262 ◽  
Author(s):  
S. Salimi ◽  
M. Salmasizadeh ◽  
M. Reza Aref

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.


2021 ◽  
Vol 73 (1) ◽  
pp. 62-67
Author(s):  
Ibrahim A. Ahmad ◽  
A. R. Mugdadi

For a sequence of independent, identically distributed random variable (iid rv's) [Formula: see text] and a sequence of integer-valued random variables [Formula: see text], define the random quantiles as [Formula: see text], where [Formula: see text] denote the largest integer less than or equal to [Formula: see text], and [Formula: see text] the [Formula: see text]th order statistic in a sample [Formula: see text] and [Formula: see text]. In this note, the limiting distribution and its exact order approximation are obtained for [Formula: see text]. The limiting distribution result we obtain extends the work of several including Wretman[Formula: see text]. The exact order of normal approximation generalizes the fixed sample size results of Reiss[Formula: see text]. AMS 2000 subject classification: 60F12; 60F05; 62G30.


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