scholarly journals Optimal multiple description transform coding of Gaussian vectors

Author(s):  
V.K. Goyal ◽  
J. Kovacevic
10.14311/852 ◽  
2006 ◽  
Vol 46 (4) ◽  
Author(s):  
M. Spiertz ◽  
T. Rusert

Multiple Description Codes (MDC) can be used to trade redundancy against packet loss resistance for transmitting data over lossy diversity networks. In this work we focus on MD transform coding based on domain partitioning. Compared to Vaishampayan’s quantizer based MDC, domain based MD coding is a simple approach for generating different descriptions, by using different quantizers for each description. Commonly, only the highest rate quantizer is used for reconstruction. In this paper we investigate the benefit of using the lower rate quantizers to enhance the reconstruction quality at decoder side. The comparison is done on artificial source data and on image data. 


Author(s):  
Ning Wang ◽  
Shuangkui Ge ◽  
Baobin Li ◽  
Lizhong Peng

Multiple description coding (MDC) is one of the source coding techniques to alleviate the problems of packet loss in the network. The decoder estimates the lost signals from received ones, based on the certain statistical correlation between descriptions. However, this correlation also leads to compression redundancy at the same time. Therefore, how to make efficient use of the introduced correlation has great importance in practical MDC approaches. In this paper, we propose a multiple description image coding scenario based on balanced multiwavelets. Two simple and effective methods to reconstruct the original image from partial descriptions are suggested. Furthermore, optimization criterion corresponding to this multiwavelet based system is provided. According to this criterion, we can choose appropriate multifilter banks to satisfy different demands. Experimental results show that the optimized multifilter banks in a simulated transform coding environment perform very well.


2002 ◽  
Vol 50 (11) ◽  
pp. 2843-2854 ◽  
Author(s):  
Y. Wang ◽  
A.R. Reibman ◽  
M.T. Orchard ◽  
H. Jafarkhani

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