Multiple description image compression based on multiwavelets

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.

Author(s):  
NING WANG ◽  
LIZHONG PENG

Multiple description coding is one of the source coding techniques used to alleviate the problems of packet loss in network. The objective is to encode a source into two (or more) bitstreams supporting two quality levels of decoding: A high-quality reconstruction decoded from the two bitstreams together, and two lower ones, but still acceptable, decoded from either of the two bitstreams individually. Most of the earlier works concentrated on the trade-off between redundancy and average side distortion, which is insufficient to evaluate the system performance. In this paper, we discuss the difference in quality between the two side reconstructions, which is defined as the balance eccentric modulus of the coding system. A scenario to design balanced multiple description coding system is presented. Application to the popular first-order autoregressive model yields encouraging results.


Author(s):  
CHUNYU LIN ◽  
YAO ZHAO ◽  
CE ZHU

In this paper, we incorporate Trellis Coded Quantization (TCQ) into a two-stage multiple description coding structure to obtain granular gain over two-stage multiple description Scalar Quantizer (SQ). Analysis and experiment on Gaussian signal show that the performance of the proposed scheme can achieve larger gain than that of the two-stage SQ scheme because of better performance of TCQ. The proposed scheme for image coding is shown to be more effective than other relevant multiple description image coding schemes in terms of central-side-distortion rate performance.


2019 ◽  
Vol 63 (5) ◽  
pp. 50401-1-50401-7 ◽  
Author(s):  
Jing Chen ◽  
Jie Liao ◽  
Huanqiang Zeng ◽  
Canhui Cai ◽  
Kai-Kuang Ma

Abstract For a robust three-dimensional video transmission through error prone channels, an efficient multiple description coding for multi-view video based on the correlation of spatial polyphase transformed subsequences (CSPT_MDC_MVC) is proposed in this article. The input multi-view video sequence is first separated into four subsequences by spatial polyphase transform and then grouped into two descriptions. With the correlation of macroblocks in corresponding subsequence positions, these subsequences should not be coded in completely the same way. In each description, one subsequence is directly coded by the Joint Multi-view Video Coding (JMVC) encoder and the other subsequence is classified into four sets. According to the classification, the indirectly coding subsequence selectively employed the prediction mode and the prediction vector of the counter directly coding subsequence, which reduces the bitrate consumption and the coding complexity of multiple description coding for multi-view video. On the decoder side, the gradient-based directional interpolation is employed to improve the side reconstructed quality. The effectiveness and robustness of the proposed algorithm is verified by experiments in the JMVC coding platform.


2011 ◽  
Vol 57 (3) ◽  
pp. 1443-1456 ◽  
Author(s):  
Jia Wang ◽  
Jun Chen ◽  
Lei Zhao ◽  
Paul Cuff ◽  
Haim Permuter

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