一种用于文本压缩的信源容错译码算法

LZ算法在文本压缩领域应用广泛。LZ译码以先前接收码字的译码结果形成字典,后续译码依赖于先前的重构数据,一旦压缩码字出现误码将会引起严重的误码扩散。分析了主流的LZ77算法编译码原理,讨论了输入误码对译码字典和解压数据的影响,研究了误码传播问题。在此基础上提出一种用于文本压缩数据的容错译码算法,指出容错处理对抑制误码传播及保证LZ77解压数据的完整性具有重要意义。     

多参考运动补偿预测中的误码传播和编码效率

视频信号在基于运动补偿预测的混合编码后,在不可靠信道中传输时会遇到严重的误码传播问题.本文从理论上分析了一种广义的运动补偿预测技术--多参考运动补偿预测的误码传播和编码效率,得到了由误码传播引起的失真和编码效率的理论极限,并给出了某些假设条件下理论极限的值.通过理论分析和对几种特定的基于多参考运动补偿预测的混合编码方案在INTERNET丢包环境中的仿真,我们发现采用多参考运动补偿预测可以同时减小误码传播失真和提高编码效率.     

长波超长波传播色散对通信解码的影响

色散效应是造成无线通信信道码间串扰的主要因素之一.就长波超长波传播色散对通信解码的影响进行了讨论.根据长波超长波在地-电离层波导中和海水中传播的模方程计算出传播色散对信号的衰减特性;分析了在典型条件下长波超长波传播色散对通信造成的误码特性;最后根据长波超长波传播色散的衰减特性提出了对其进行补偿的有效方法,使传播色散对通信误码的影响减小到最低程度.     

中容量数字微波通信系统用1:1无损伤倒换设备

本文阐述了数字微波倒换设备与模拟微波倒换设备的主要差别,简要介绍了数字微波倒换设备的特点,并就无损伤倒换设备中不停机误码检测方法以及自动吸收传播时延的自适应时延调节方法等,作了较为详细的论述。介绍了设备的主要技术性能及组成情况,还对采用终端机的帧信号作信道机误码检测用帧信号时所产生的门限误码处系统发生瞬断的现象作了探讨,所得结果对研制中大容量数字微波系统选择不停机误码检测方式,有一定的参考价值。     

一种抗误码能力强的视频压缩SPIHT改进算法

基于小波域的3D-SPIHT算法是一种高效的视频压缩编码方法,但其抗误码能力仍有待提高.本文提出了一种改进的3-D DWT-SPIHT分树编码方法.在不改变原算法小波树结构的前提下,插入编解码链表同步点,将整个帧组分为多棵子树独立编码和传输,从而将差错传播局限在每棵独立子树内,控制了差错传播范围.通过仿真实验证明了该算法与原算法相比具有较高的抗误码能力.     

基于DVB-T2的旋转星座解映射算法及其改进

在广播和通信领域最新概念中,星座旋转作为一种分集技术被应用于DVB-T2标准。对于旋转星座采用基于硬判决的二维迭代LLR解映射算法进行了一些简化,但由于硬判决带来了误码传播,并且首次软信息计算仍较为复杂。提出了两种改进方案,分别从误码性能和运算复杂度方面对原算法进行了改进。仿真结果表明,信道条件较为恶劣时,改进方法在性能上优于原系统。     

基于VBLAST—OFDM的平行循环迭代QR分解检测算法

论文提出了一种基于VBLAST—OFDM系统的平行循环迭代QR分解检测算法,称为P-IQRD算法。该算法主要克服循环迭代QR分解算法最先检测层性能差的缺点,将平行算法的思想引入到循环迭代QR分解算法中,进一步抑制了误码传播现象,有效提高了系统的检测性能。仿真结果表明,文中提出的P-IQRO算法比循环迭代QR分解算法、传统的平行算法、QR算法和迫零算法在误码性能上要好。     

关于SDH传输设备误码问题的处理探讨

本文简要介绍了分析误码的理论、检测误码的原理、造成误码的因素,结合实际提出了处理误码问题的具体措施并且举例说明。     

基于FPGA的突发误码测试仪的设计

传统误码测试仪通过统计误码比特数,采用长期平均误码率为指标,这在分析评估频繁突发误码信道性能时有一定局限性.文章提出了一种基于FPGA的突发误码测试仪设计方案,将突发误码持续时间、误码间隔时间及突发误码起止时刻作为性能评价指标,给出了误码持续长度检测的仿真结果,指出了位同步和序列同步是设计的关键.     

基于H.263视频解码的误码掩盖技术研究

本文提出了一种基于接收端像素域的误码掩盖算法,包括误码检测、重同步、误码定位和估计误码数据等四个环节。计算机模拟结果显示,经误码掩盖之后的图像质量有显著的改善     

EDA-78: A Novel Error Detection Algorithm for Lempel-Ziv-78 Compressed Data

Wireless Personal Communications - This paper presents an error detection algorithm for Lempel-Ziv-78 (LZ78) compressed data. LZ78 data compression involves dictionary coding and aims to compress...     

水声通信数据无损压缩方法的对比研究

为了选择适合水声通信数据无损压缩的算法,对哈夫曼压缩算法和LZ77压缩算法进行了对比研究。通过C语言编程实现两种算法的压缩,并利用水声通信数据获得压缩结果。对两种算法的压缩率和压缩效率对比分析之后,得出结论：对于水声信号,使用哈夫曼算法将获得更好的压缩率和压缩速率。尤其是哈夫曼算法的压缩速率远远优于LZ77算法。     

Robust data compression applied to rotary head DDS-3 tapes

A piecewise, lossless data compression scheme which limits the propagation of errors is used to compress text data. The error control coding of DDS is then applied to the compressed test data using a computer simulation. Burst channel errors representing longitudinal tape defects are simulated. It is shown that when data containing uncorrected errors is decompressed, the proposed strategy curtails error propagation with only a small drop-off in compression ratio     

Lossless compression of VLSI layout image data.

We present a novel lossless compression algorithm called Context Copy Combinatorial Code (C4), which integrates the advantages of two very disparate compression techniques: context-based modeling and Lempel-Ziv (LZ) style copying. While the algorithm can be applied to many lossless compression applications, such as document image compression, our primary target application has been lossless compression of integrated circuit layout image data. These images contain a heterogeneous mix of data: dense repetitive data better suited to LZ-style coding, and less dense structured data, better suited to context-based encoding. As part of C4, we have developed a novel binary entropy coding technique called combinatorial coding which is simultaneously as efficient as arithmetic coding, and as fast as Huffman coding. Compression results show C4 outperforms JBIG, ZIP, BZIP2, and two-dimensional LZ, and achieves lossless compression ratios greater than 22 for binary layout image data, and greater than 14 for gray-pixel image data.     

Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis

Lempel-Ziv complexity (LZ) and derived LZ algorithms have been extensively used to solve information theoretic problems such as coding and lossless data compression. In recent years, LZ has been widely used in biomedical applications to estimate the complexity of discrete-time signals. Despite its popularity as a complexity measure for biosignal analysis, the question of LZ interpretability and its relationship to other signal parameters and to other metrics has not been previously addressed. We have carried out an investigation aimed at gaining a better understanding of the LZ complexity itself, especially regarding its interpretability as a biomedical signal analysis technique. Our results indicate that LZ is particularly useful as a scalar metric to estimate the bandwidth of random processes and the harmonic variability in quasi-periodic signals.     

远程控制中视频信号压缩问题的解决方案

描述JPEG和LZ77压缩算法原理．并通过实际软件仿真进行比较，认证了DSP(Digital Signal Processor)适合于JPEG图像压缩算法。然后通过计算机和DSP的JPEG压缩速度的比较，提出了远程控制中视频信号压缩问题的解决方案。     

The Universal LZ77 Compression Algorithm Is Essentially Optimal for Individual Finite-Length $N$-Blocks

Consider the case where consecutive blocks of N letters of a semi-infinite individual sequence X over a finite alphabet are being compressed into binary sequences by some one-to-one mapping. No a priori information about X is available at the encoder, which must therefore adopt a universal data-compression algorithm. It is known that there exist a number of asymptotically optimal universal data compression algorithms (e.g., the Lempel-Ziv (LZ) algorithm, context tree algorithm and an adaptive Hufmann algorithm) such that when successively applied to N-blocks then, the best error-free compression for the particular individual sequence X is achieved as N tends to infinity. The best possible compression that may be achieved by any universal data compression algorithm for finite N-blocks is discussed. Essential optimality for the compression of finite-length sequences is defined. It is shown that the LZ77 universal compression of N-blocks is essentially optimal for finite N-blocks. Previously, it has been demonstrated that a universal context tree compression of N blocks is essentially optimal as well.     

Gzip压缩的硬件加速电路设计

硬件无损压缩技术可以发挥专用电路的速度和功耗优势,被广泛应用于大数据计算以及通信领域.本文以GNUzip（Gzip）数据无损压缩技术为原型设计了一种硬件压缩电路.通过采用双Hash函数、并行匹配处理、面向硬件存储的LZ77压缩存储格式、高效数据拼接器等加速方法,发挥并行计算和流水线结构优势,提升压缩速率.该硬件压缩电路基于Verilog HDL设计,使用现场可编程门阵列（FPGA）进行测试和验证.测试数据表明：与软件压缩方式相比,该硬件压缩电路在获得适中压缩率（65.9%）的同时,其压缩速率得到显著提升,平均压缩速率达171Mb/s,满足网络通信、数据存储等实时压缩应用需求.     

On Universal Data Compression—An Intuitive Overview

The sliding-window version of the Lempel-Ziv data-compression algorithm (LZ1) has found many applications recently (e.g., the Stacker program for personal computers and the new Microsoft MS-DOS.6.2). Other versions of the Lempel-Ziv data-compression algorithm (LZ2) became an integral part of international standards for data transmission modems and proved themselves to be highly successful. The purpose of this paper is to give an intuitive overview of universal, noiseless data compression of sequences as well as 2-D images, by following the lines of approach which characterizes the family of LZ universal codes and by further extending this approach so as to yield some new results.