毕业论文外文翻译一种基于嵌入式零树小波算法的鲁棒图像压缩新方法

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1、外 文 翻 译毕业设计题目：基于EZW算法的图像压缩研究与实现原文1：A New Method of Robust Image Compression Based on the Embedded Zerotree Wavelet Algorithm（一）译文1： 一种基于嵌入式零树小波算法的鲁棒图像压缩新方法（一）原文2： A New Method of Robust Image Compression Based on the Embedded Zerotree Wavelet Algorithm（二）译文2： 一种基于嵌入式零树小波算法的鲁棒图像压缩新方法（二）A New Method o

2、f Robust Image Compression Based onthe Embedded Zero tree Wavelet Algorithm（一）Charles D. CreusereAbstractWe propose here a wavelet-based image compression algorithm that achieves robustness to transmission errors by partitioning the transform coefficients into groups and independently processing eac

3、h group using an embedded coder. Thus, a bit error in one group does notaffect the others, allowing more uncorrupted information to reach the decoder.Index TermsCoefficient partitioning, embedded bitstream, error resilience,image compression, low complexity, wavelets.I. INTRODUCTIONRecently, the pro

4、liferation of wireless services and the internet along with consumer demand for multimedia products has spurred interest in the transmission of image and video data over noisy communications channels whose capacities vary with time. In such applications, it can be advantageous to combine the source

5、and channel coding (i.e., compression and error correction) processes from both a complexity and an information theory standpoint .In this work, we introduce a form of low-complexity joint source channel coding in which varying amounts of transmission error robustness can be built directly into an e

6、mbedded bit stream. The approach taken here modifies Shapiros embedded zerotree wavelet (EZW) image compression algorithm , but the basic idea can be easily applied to other wavelet-based embedded coders suchThis paper is organized as follows. In Section II, we discuss the conventional EZW image com

7、pression algorithm and its resistance to transmission errors. Next, Section III develops our new, robust coder and explores the options associated with its implementation. In Section IV, we analyze the performance of the robust algorithm in the presence of channel errors, and we use the results of t

8、his analysis to perform comparisons in Section V. Finally, implementation and complexity issues are discussed in Section VI, followed by conclusions in Section VII.II. EZW IMAGE COMPRESSIONAfter performing a wavelet transform on the input image, the EZW encoder progressively quantizes the coefficien

9、ts using a form of bit plane coding to create an embedded representation of the imagei.e.,a representation in which a high resolution image also contains all coarser resolutions. This bit plane coding is accomplished by comparing the magnitudes of the wavelet coefficients to a threshold T to determi

10、ne which of them are significant: if the magnitude is greater than T, that coefficient is significant. As the scanning progresses from low to high spatial frequencies, a 2-b symbol is used to encode the sign and position of all significant coefficients. This symbol can be a + or - indicating the sig

11、n of the significant coefficient; a “0” indicating that the coefficient is insignificant; or a zerotree root (ZTR) indicating that the coefficient is insignificant along with all of the finer resolution coefficients corresponding to the same spatial region. The inclusion of the ZTR symbol greatly in

12、creases the coding efficiency because it allows the encoder to exploit interscale correlations that have been observed in most images . After computing the “significance map” symbols for a given bit plane, resolution enhancement bits must be transmitted for all significant coefficients; in our imple

13、mentation, we concatenate two of these to form a symbol. Prior to transmission, the significance and resolution enhancement symbols are arithmetically encoded using the simple adaptive model described in with a four symbol alphabet (plus one stop symbol). The threshold T is then divided by two, and

14、the scanning process is repeated until some rate or distortion target is met. At this point, the stop symbol is transmitted. The decoder, on the other hand, simply accepts the bitstream coming from the encoder, arithmetically decodes it, and progressively builds up the significance map and enhanceme

15、nt list in the exact same way as they were created by the encoder. The embedded nature of the bitstream produced by this encoder provides a certain degree of error protection. Specifically, all of the information which arrives before the first bit error occurs can be used to reconstruct the image; e

16、verything that arrives after is lost. This is in direct contrast to many compression algorithms where a single error can irreparably damage the image. Furthermore, we have found that the EZW algorithm can actually detect an error when its arithmetic decoder terminates (by decoding a stop symbol) bef

17、ore reaching its target rate or distortion. It is easy to see why this must happen. Consider that the encoder and decoder use the same backward adaptive model to calculate the probabilities of the five possible symbols (four data symbols plus the stop symbol) and that these probabilities directly de

18、fine the codewords. Not surprisingly, the length of a symbols codeword is inversely proportional to its probability. If a completely random bit sequence is fed into the arithmetic decoder, then the probability of decoding any symbol is completely determined by the initial state of the adaptive model

19、i.e., the probability weighting defined by the model is not, on the average, changed by a random input.In our implementation of the Witten et al. arithmetic coder, we set Max_frequency equal to 500 and maintain the stop symbol probability at 1/cum_freq. Because cum_freq (the sum of the frequency cou

20、nts of all symbols) is divided by two whenever it exceeds Max_frequency, the probability of decoding a stop symbol stays mostly between 1/250 and 1/500. Thus, if a random bitstream is fed into the decoder after training it to this point, an average of 250 to 500 symbols will be processed before the

21、stop symbol is decoded. The bitstream is correctly interpreted as long as the decoder is synchronized with the encoder, but this synchronization is lost shortly after the first error occurs. Once this happens, the incoming bitstream looks random to the decoder (the more efficient the encoder, the mo

22、re random it will appear). Since each symbol is represented in the compressed image by between one and two bits, the decoder should self-terminate between 31 and 125 bytes after an error occurs. Experimentally, we have found that the arithmetic decoder overrun is typically between 30 and 50 bytes, w

23、hich is consistent with the theoretical range, since most of these terminations took place while decoding the highly compressed significance map. If the overrun is small compared to the number of bits correctly decoded, it does not significantly affect the quality of the reconstructed image. While s

24、ome erroneous information is incorporated into the wavelet coefficients, the bit plane scanning structure ensures that it is widely dispersed spatially, making it visually insignificant in the image.5作者：Charles D. Creusere国籍: 美国出处：图像处理 电机及电子学工程师联合会 1997年第10期1436-1442页 ISSN1057-7149一种基于嵌入式零树小波算法的鲁棒图像

25、压缩新方法（一）Charles D. Creusere摘要-本文We propose a wavelet-based image compression algorithm that achieves robustness to transmission errors by partitioning the transform coefficients into groups and independently processing each group using an embedded coder.提出了一种基于小波变换的图像压缩算法，通过分割由系数转变成的群组和独立使用内嵌编码器处理每个

26、分组，实现了传输错误的鲁棒性。这样，一个分组的误码不会影响到其他的分组，允许更多未损坏的信息到达解码器 标引词Thus, a bit error in one group does not affect the others, allowing more uncorrupted information to reach the decoder-系数分割，内嵌码流，错误恢复，图像压缩，低复杂性，小波。一、引言最近，受到无线服务和互联网的增殖、消费者对多媒体产品的需求的影响，人们对图像和视频数据在含噪通信信道的传输的兴趣日益加深。基于这种应用环境，无论从复杂性还是信息理论的角度，合并信源和信道编码

27、（即压缩和纠错）流程都是十分有利的。在这项工作中，我们采用了一种低复杂度、信源信道联合编码的形式，不同数量的传播错误的鲁棒性可直接内建在一个内嵌比特流中。在这里采用的方法修改了Shapiro的嵌入式零树小波（零树）图像压缩算法，但其基本思想可以很容易地适用于其他基于小波变换的嵌入式编码，例如Said and Pearlman 的基于小波变换的嵌入式编码和Taubman and Zakho的基于小波变换的嵌入式编码。使用这种方法已经得到了一些初步成果。 本文组织如下。在第二节，我们将讨论传统的EZW图像压缩算法及其对传输误差的影响。接下来，我们在第三节开发新的鲁棒性编码器并且探索相关实施编码器的

28、方案。在第四节，我们分析在存在通道错误的情况下鲁棒EZW算法的性能，并且我们在第五节使用这种分析的结果充当对比。最后，在第六节中讨论执行和复杂性问题，在第七节记录随后的结论。二、 EZW图像压缩一幅输入的图像经过小波变换后，EZW编码器利用位平面编码创造一个图像的嵌入式描绘的形式来逐步量化系数，也就是说，高分辨率的描绘还包含所有粗分辨率。此位平面编码是通过比较小波系数和阈值T的大小来确定哪些系数是重要的：如果小波系数比T大，则这个小波系数是重要的。由于扫描进程是从低空间频率到高空间频率，一个2bit的字符被用来编码所有重要系数的标记和位置。这个字符可以是用+或-来描述重要系数的标记；一个“0”

29、表明该系数是微不足道的;一个零树的根（ZTR）表明该系数与在同一空间区域中所有高分辨度相应的系数一样是微不足道的。ZTR字符的引入大大提高了编码效率，因为它允许编码器利用已在大多数图像中被观察到的层间之间的相关性。在计算操作给定位平面的“重要性图表”字符后，分辨率增强位必须被传输到所有重要的系数，在我们的实际操作中，我们连接两个系数来形成字符。在传输之前，重要性图表字符和分辨率增强字符使用简单的自适应模型描述 ，以四个字母符号来进行算术编码（增加一个停止字符）。然后阈值T除以2，并且反复进行扫描过程直至满足某一码率或失真率目标。此时，传输停止符号。另一方面，解码器仅仅只接收来自编码器的比特流，

30、对其进行解码，并逐步用和编码器建立重要性图表的列出方式完全相同的列出方式建立重要性图表和增强字符。编码器产生的比特流的内嵌性质提供一定程度上的误差防护。具体而言，第一个位错误到达之前的所有信息可以用来重建图像;一切在丢失后都可以到达。这是许多一个错误就可能对图像造成不可弥补的损害的压缩算法所不能相比的。此外，我们已经发现，EZW算法实际上在实现其目标码率或失真之前，当其解码器终止（通过解码停止符号）的时候可以检测到存在的错误。我们很容易理解为什么这项算法功能必须存在。我们考虑编码器和解码器使用相同的落后的自适应模型来计算5个可能字符（4数据字符加上停止字符）的概率，并且这些概率直接确定码字。毫

31、不奇怪， 一个字符的码字长度和其概率是成反比的。如果将一个完全随机位序列送入解码器，那么任何字符的解码概率完全取决于自适应模型的初始状态，也就是说，一般来说，一个随机的输入并不改变概率权重的模型定义。在我们使用Wittenetal编码器时，我们设置Max_frequency为500且保持停止字符概率1/cum_freq 。由于cum_freq（所有的字符的频率的总和）每逢超过Max_frequency都要除以2，译码停止字符的概率主要是在1/2501/500之间。因此，如果在到达这一点后将一个随机码流是送入解码器，在编码停止字符之前平均250到500个字符会被处理。比特流可以正确理解为编码器和

32、解码器的同步，但这种同步在第一个错误发生后不久将会失去。一旦发生这种情况，比特流将被看做随机输入送入解码器（越是有效率的编码器，越会出现随机输入）。因为每个符号在压缩图像中代表1至2位，编码器错误发生后的31至125字节时会自行终止。通过实验，我们发现，解码器溢出通常是在30至50字节时，它与理论范围是一致的，因为这些终止会发生在解码高度压缩的重要图的时侯。如果溢出和正确解码的位数相比是比较小的，它并不会显着影响的重建图像的质量。虽然一些错误的信息会被纳入小波系数中，位平面扫描结构会确保它被广泛地分散在空间中，使其在图像中毫无意义。A New Method of Robust Image Co

33、mpression Based onthe Embedded Zerotree Wavelet Algorithm（二）Charles D. CreusereIII. ROBUST EZW (REZW) ALGORITHMThe basic idea of the REZW image compression algorithm is to divide the wavelet coefficients up into S groups and then to quantize and code each of them independently so that S different em

34、bedded bitstreams are created. These bitstreams are then interleaved as appropriate (e.g., bits, bytes, packets, etc.) prior to transmission so that the embedded nature of the composite bitstream is maintained. In the remainder of this paper we assume that individual bits are interleaved. For the RE

35、ZW approach to be effective, each group of wavelet coefficients must be of equal size and must uniformly span the image. A similar method has been proposed in to parallelize the EZW algorithm, but that method instead groups the coefficients so that data transmission between processors is minimized.

36、What do we gain by using this new algorithm over the conventional one? As has been pointed out in Section II, the EZW decoder can use all of the bits received before the occurrence of the first error to reconstruct the image. By coding the wavelet coefficients with multiple, independent (and interle

37、aved) bit streams, a single bit error truncates only one of the streamsthe others are still completely received. Consequently, the wavelet coefficients represented by the truncated stream are reconstructed at reduced resolution while those represented by the other streams are reconstructed at the fu

38、ll encoder resolution. If the set of coefficients in each stream spans the entire image, then the inverse wavelet transform in the decoder evenly blends the different resolutions so that the resulting image has a spatially consistent quality.IV. STOCHASTIC ANALYSISTo evaluate the effectiveness of th

39、is family of robust compression algorithms, we assume that the coded image is transmitted through a binary symmetric, memoryless channel with a probability of bit error given by. We would like to know the number of bits correctly received in each of the S streams. Since this quantity is itself a ran

40、dom variable, we use its mean value to characterize the performance of the different algorithms. Because the channel is memoryless, streams terminate independently of each other, but the mean values of their termination points are always the same for a specified. Assuming that the image is compresse

41、d to B total bits and that S streams are used, then the probability of receiving k of the B/S bits in each stream correctly is given by (2)which is a valid probability mass function as one can easily verify by summing over all k: In (2), is the probability that the first k bits are correct whileis t

42、he probability that the (k + 1)th bit is in error. Note that a separate term conditioned on B/S is necessary to take into account the possibility that all of the bits in the stream are correctly received. The mean value can now be calculated as (3)On the average, the total number of bits correctly r

43、eceived is. If B/S is large relative to1/, then . Generally, the gain actually achieved is not this high, but it is nonetheless significant. In Section V, we use (3) to analyze the impact of transmission errors on the average quality of the reconstructed image for all possible values of S.作者：Charles

44、 D. Creusere国籍: 美国出处：图像处理 电机及电子学工程师联合会 1997年第10期1436-1442页 ISSN1057-7149一种基于嵌入式零树小波算法的鲁棒图像压缩新方法（二）Charles D. Creusere三、鲁棒嵌入式零树小波（REZW）算法该REZW图像压缩算法的基本思想是将小波系数分成S组然后每组逐个独立的进行量化和编码，于是便创建了S组不同的内嵌比特流。然后在传输之前将这些码流适当交叉（如，位，字节，包等），使混合比特流的内嵌性质得以维持。在本文的其余部分，我们假设个别位交错。为了使REZW算法有效，每个小波系数组必须是同样大小并且均一地跨越图像。类似的方法

45、已被建议使用来并行EZW算法，若非这种使用分组代替系数的算法，处理器之间的数据传输也不会最小化。我们使用这种新算法代替传统的算法究竟会有什么增益？正如在第二部分所指出的那样，EZW解码器可以利用在第一个错误出现前接收到的所有位来重建图像。通过将小波系数编码成多个独立（交错）的比特流，一个单一的位错误只截断比特流中的一个，其余的比特流仍可以被完整地接收到。因此，被截断的比特流的小波系数的描绘重建为不完整的分辨率而那些其他比特流的描绘都重建为完整地编码器分辨率。如果每个比特流的一组系数都跨越整个图像，那么在解码器中的逆小波变换必须均匀地混合不同的分辨率，这样产生的图象在空间上才会拥有符合标准的质量

46、。四、随机分析为了评估这一系列鲁棒压缩算法的效率，我们假设利用一种二元对称、无记忆并且伴随着可能由引起的比特错误的信道来传输编码图像。我们希望知道S组比特流中每组准确地接收到的比特数。由于这个数值本身是一个随机变量，我们使用其均值来描述不同算法的性能。由于信道的无记忆性，比特流彼此独立地终止，但其终结点的平均值总是相同的指定值。假设图像被压缩为总数为B的比特并且将其分成S组，那么每个数据流中数值B/S个比特的接收概率k可以通过公式 （2）准确得到。这是一个有效的概率函数，可以通过对所有k值求和来验证其有效性。在公式（2）中，表示前k个比特传输正确的概率，而表示第k+1个比特传输错误的概率。请注

47、意，考虑到比特流中的所有位都是正确接收的可能性，提出一个单独的基于B/S术语是必要的。平均值现在可以通过公式 （3）来计算。一般来说，正确收到的比特总数是。如果B/S是远远大于1/，则。一般来说，实际所能达到的增益并没有那么高，但是这也是十分重要的。在第五节中，我们使用公式（3）分析所有可能的S值下，平均传输误差对重建图像质量的影响。五分钟搞定5000字毕业论文外文翻译，你想要的工具都在这里!在科研过程中阅读翻译外文文献是一个非常重要的环节，许多领域高水平的文献都是外文文献，借鉴一些外文文献翻译的经验是非常必要的。由于特殊原因我翻译外文文献的机会比较多，慢慢地就发现了外文文献翻译过程中的三大利

48、器：Google“翻译”频道、金山词霸（完整版本）和CNKI“翻译助手。具体操作过程如下： 1.先打开金山词霸自动取词功能，然后阅读文献； 2.遇到无法理解的长句时，可以交给Google处理，处理后的结果猛一看，不堪入目，可是经过大脑的再处理后句子的意思基本就明了了； 3.如果通过Google仍然无法理解，感觉就是不同，那肯定是对其中某个“常用单词”理解有误，因为某些单词看似很简单，但是在文献中有特殊的意思，这时就可以通过CNKI的“翻译助手”来查询相关单词的意思，由于CNKI的单词意思都是来源与大量的文献，所以它的吻合率很高。 另外，在翻译过程中最好以“段落”或者“长句”作为翻译的基本单位，

49、这样才不会造成“只见树木，不见森林”的误导。四大工具： 1、Google翻译： google，众所周知，谷歌里面的英文文献和资料还算是比较详实的。我利用它是这样的。一方面可以用它查询英文论文，当然这方面的帖子很多，大家可以搜索，在此不赘述。回到我自己说的翻译上来。下面给大家举个例子来说明如何用吧比如说“电磁感应透明效应”这个词汇你不知道他怎么翻译，首先你可以在CNKI里查中文的，根据它们的关键词中英文对照来做，一般比较准确。 在此主要是说在google里怎么知道这个翻译意思。大家应该都有词典吧，按中国人的办法，把一个一个词分着查出来，敲到google里，你的这种翻译一般不太准，当然你需要验证是

50、否准确了，这下看着吧，把你的那支离破碎的翻译在google里搜索，你能看到许多相关的文献或资料，大家都不是笨蛋，看看，也就能找到最精确的翻译了，纯西式的！我就是这么用的。 2、CNKI翻译： CNKI翻译助手，这个网站不需要介绍太多，可能有些人也知道的。主要说说它的有点，你进去看看就能发现：搜索的肯定是专业词汇，而且它翻译结果下面有文章与之对应（因为它是CNKI检索提供的，它的翻译是从文献里抽出来的），很实用的一个网站。估计别的写文章的人不是傻子吧，它们的东西我们可以直接拿来用，当然省事了。网址告诉大家，有兴趣的进去看看，你们就会发现其乐无穷！还是很值得用的。 3、网路版金山词霸（不到1M）：

51、 4、有道在线翻译：翻译时的速度：这里我谈的是电子版和打印版的翻译速度，按个人翻译速度看，打印版的快些，因为看电子版本一是费眼睛，二是如果我们用电脑，可能还经常时不时玩点游戏，或者整点别的，导致最终SPPEED变慢，再之电脑上一些词典（金山词霸等）在专业翻译方面也不是特别好，所以翻译效果不佳。在此本人建议大家购买清华大学编写的好像是国防工业出版社的那本英汉科学技术词典，基本上挺好用。再加上网站如：google CNKI翻译助手，这样我们的翻译速度会提高不少。具体翻译时的一些技巧（主要是写论文和看论文方面） 大家大概都应预先清楚明白自己专业方向的国内牛人，在这里我强烈建议大家仔细看完这些头上长角

52、的人物的中英文文章，这对你在专业方向的英文和中文互译水平提高有很大帮助。 我们大家最蹩脚的实质上是写英文论文，而非看英文论文，但话说回来我们最终提高还是要从下大工夫看英文论文开始。提到会看，我想它是有窍门的，个人总结如下： 1、把不同方面的论文分夹存放，在看论文时，对论文必须做到看完后完全明白（你重视的论文）；懂得其某部分讲了什么（你需要参考的部分论文），在看明白这些论文的情况下，我们大家还得紧接着做的工作就是把论文中你觉得非常巧妙的表达写下来，或者是你论文或许能用到的表达摘记成本。这个本将是你以后的财富。你写论文时再也不会为了一些表达不符合西方表达模式而烦恼。你的论文也降低了被SCI或大牛刊物退稿的几率。不信，你可以试一试 2、把摘记的内容自己编写成检索，这个过程是我们对文章再回顾，而且是对你摘抄的经典妙笔进行梳理的重要阶段。你有了这个过程。写英文论文时，将会有一种信手拈来的感觉。许多文笔我们不需要自己再翻译了。当然前提是你梳理的非常细，而且中英文对照写的比较详细。 3、最后一点就是我们往大成修炼的阶段了，万事不是说成的，它是做出来的。写英文论文也就像我们小学时开始学写作文一样，你不练笔是肯定写不出好作品来的。所以在此我鼓励大家有时尝试着把自己的论文强迫自己写成英文的，一遍不行，可以再修改。最起码到最后你会很满意。呵呵，我想我是这么觉得的。