和志圆,
王炯
云南大学信息学院电子工程系 昆明 650500
基金项目:国家自然科学基金(61861045)
详细信息
作者简介:陈建华：男，1964年生，教授，博士生导师，研究方向为信息传输理论与应用
和志圆：男，1994年生，硕士生，研究方向为分布式信源编码
王炯：女，1994年生，硕士生，研究方向为Context建模
通讯作者:陈建华 chenjh@ynu.edu.cn
中图分类号:TN911.21; TN919.81计量
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被引次数:0
出版历程
收稿日期:2019-07-11
修回日期:2020-03-17
网络出版日期:2020-04-15
刊出日期:2020-07-23
Distributed Source Coding Using Improved Side Information
Jianhua CHEN,,Zhiyuan HE,
Jiong WANG
School of Information Science and Engineering, Yunnan University, Kunming 650500, China
Funds:The National Natural Science Foundation of China (61861045)
摘要
摘要:针对现有的非对称分布式信源编码(DSC)方案均存在的在误比特率(BER)以及压缩率方面的不足，该文提出基于边信息改进的DSC(DSCUISI)方案。发送方对信源序列进行抽样，将序列分为抽样与未抽样子序列，利用算术编码器对未抽样子序列进行压缩，同时计算抽样子序列的伴随式。接收方利用边信息序列与未抽样子序列之间的相关性，对抽样符号进行估计，估计出的序列与原始抽样子序列的相关性得到改进。最后利用原始抽样子序列的伴随式与估计出的序列进行联合译码以重建原始抽样子序列。实验结果表明：与基于低密度奇偶校验码和算术码的DSC方案相比，该文所提方案在信源内部相关性较强时具有压缩率高、在信源间相关度不高时则有重建错误率低的特点，是一种高效、实用且易于实现的DSC方案。
关键词:分布式信源编码/
边信息/
离散去噪理论/
抽样
Abstract:Considering the shortcomings on the Bit Error Rate (BER) and the compression ratio of the existing asymmetric Distributed Source Coding (DSC) schemes, a scheme named Distributed Source Coding Using Improved Side Information (DSCUISI) is proposed. At the sender, the source sequence is sampled and divided into a sampled and an un-sampled sub-sequences. The un-sampled sub-sequence is compressed by arithmetic coder while the syndrome of the sampled sub-sequence is calculated. The receiver exploits the correlation between the side information and the un-sampled sub-sequence to estimate the sampled symbols, so that the correlation between the estimated sequence and the original sampled sub-sequence is improved. Finally, the syndromes and the estimated sequence are used to recover the sampled sub-sequence. Experiment results show that the DSCUISI can reach high compression ratio, when the correlation among neighboring symbols is strong. The BER of the reconstructed sequence can be kept low when the correlation between sources are weak. It is an efficient, practical DSC scheme and is easy to be implemented.
Key words:Distributed Source Coding(DSC)/
Side Information(SI)/
Discrete denoising theory/
Sampling
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