星载SAR原始数据普适性分块自适应矢量压缩算法

传统矢量量化器的码本普适性差,需要在线更新,难以在星载SAR系统中实现.文中针对星载SAR原始数据的统计特性,以多维空间上的失真函数为代价函数,根据输入数据的联合概率密度函数设计得到了具有普适性的矢量量化码本,分析了原始数据矢量量化编码以及解码方案.在此基础上,深入研究了矢量量化级联熵编码方案的可行性以及码字索引在信道传输发生误码时算法的稳健性.实际数据处理结果表明,文中算法具有普适性,矢量量化码本的普适性使得码本可以进行离线设计,为矢量量化的星载实用化提供了理论指导.     

神经网络矢量量化技术在语音编码中的应用

讨论了在语音编码中,应用神经网络技术进行矢量量化的算法。神经网络矢量量化算法可以压缩码本维数,提高码本搜索速度,从而优化矢量量化的效果。将这种优化的矢量量化算法应用于语音编码中,能降低运算复杂度,提高编码质量。     

矢量量化压缩图像中的安全隐写方法

提出一种用于矢量量化压缩图像的安全数据隐藏方案.为降低数据嵌入引入的失真,以码字间的矢量均方差为优化指标,采用遗传算法实现码本的优化分割,并提出基于码本分割的数据嵌入算法.采用基于自适应算术熵解码的数据映射方法,实现了嵌入前后统计特性的保持.实验结果表明,所提出的算法在容量、失真水平和安全性方面具有较好的综合性能.     

小波树结构快速矢量量化编码方法

提出了基于人眼视觉属性和应用小波树结构２快速图象编码的矢量量化图象编码方法，简称为树结构快速矢量量化编码。在分析此方法矢量量化特点之后，设计产生码本的统计方法，并提出了矢量量化编码的快速算法。     

一种增强的LPC参数多级矢量量化技术

为充分利用码本的级间相关性,提出了一种联合码本优化多级矢量量化(JCO-MSVQ)码本设计方法。每次迭代时,先将训练矢量对码字进行聚类,再对各级码本进行联合优化,利用条件期望逐级更新码本。实验数据表明,该算法在设计10维线谱频率(LSF)参数量化码本时,较随机松弛算法(SR)码本有更小的平均量化畸变。23比特/帧LSF参数量化器平均对数谱失真为0.87dB,达到了透明量化要求。     

线谱对参数预测多级矢量量化联合优化算法

提出了一种线谱对参数预测多级矢量量化联合优化算法.该算法对预测系数和多级矢量量化采用渐进闭环设计,通过迭代实现预测系数和多级矢量量化设计的联合优化.在多级矢量量化设计过程中,采用迭代优化实现多级码本的联合优化.采用语音线谱对参数对量化算法进行测试.测试结果表明,与传统算法相比,该量化算法可以减小线谱对参数量化失真.提高编码语音质量.     

矢量量化压缩图像中的安全隐写方法

提出一种用于矢量量化压缩图像的安全数据隐藏方案 .为降低数据嵌入引入的失真,以码字间的矢量均方差为优化指标,采用遗传算法实现码本的优化分割,并提出基于码本分割的数据嵌入算法. 采用基于自适应算术熵解码的数据映射方法,实现了嵌入前后统计特性的保持 .实验结果表明,所提出的算法在容量,失真水平和安全性方面具有较好的综合性能.

     

基于自组织特征映射的图象矢量量化

本文就基于自组织特征映射的图象矢量量化编码做了初步的探讨,得出一些结论。在矢量量化中,码本性能的好坏对重建的图像有直接的影响。我们利用自组织特征映射(SOFM)网络进行聚类,实现了图像矢量码本的生成,然后再根据矢量量化(VQ)编码原理将图像重建。该方法可以达到较高的压缩比,实现了图像压缩。并且,就不同条件下的图像作了对比。     

说话人识别中基于聚类特征的矢量量化技术

为解决采用矢量量化的方法进行说话人识别时出现的失真问题,根据汉语语音的发音特性,提出了将矢量量化与语音特征的聚类技术相结合的方法,在进行矢量量化码书训练之前,先对特征矢量进行聚类筛选。实验结果表明,当测试语音片段长度为4 s时,在保持95％左右识别率下,采用普通矢量量化方法需64码本数,而采用该文方法只需8码本数,降低了8倍。结果说明该方法不但在一定程度上解决了因训练样本不足而引起的失真问题,而且通过方法的改进,实现了采用较低码字数产生较好的识别结果,从而提高识别效率。     

自适应模糊聚类LBG矢量量化算法

采用模糊聚类C均值聚类确定型心改进LBG算法,实现语音参数MFCC码本的矢量量化,实验结果表明,该算法有着与单一LBG算法相近的量化误差,自适应确定码本大小码,码本尺寸显著降低,减小码本的存储量。     

改进的分形矢量量化编码

为了提高图象的分形矢量量化编码效果，在利用四叉树对图象进行自适应分割的基础上，基于正交基三维分量投影准则，提出了图象块非平面近似方法，进而形成一种新的静态图象分形矢量量化编码方法。该方法首先通过对投影参数进行DPCM编码来构造粗糙图象，然后由此来构成差值图象编码的码书。由于该方法把分形和矢量量化编码结合起来，因此解码时只需查找码书，并仅进行对比度变换。计算机编、解码实验结果表明，该编码方法具有码书不需外部训练，解码也不需迭代等优点，且与其他同类编码器相比，该方法在压缩比和恢复图象质量（PSRN）方面均有明显改善。     

Low bit-rate multi stage vector quantization based on energy clustered training set

In this paper, a new multi stage vector quantization with energy clustered training set is proposed for color image coding. The input image is applied with orthogonal polynomials based transformation and the energy clustered transformed training vectors are obtained with reduced dimension. The stage-by-stage codebook for vector quantization is constructed from the proposed transformed training vectors so as to reduce computational complexity. This method also generates a single codebook for all the three color components, utilizing the inter-correlation property of individual color planes and interactions among the color planes due to the proposed transformation. As a result, the color image encoding time is only slightly higher than that of gray scale image coding time and in contrast to the existing color image coding techniques, whose time is thrice greater than that of gray scale image coding. The experimental results reveal that only 35 % and 10 % of transform coefficients are sufficient for smaller and larger blocks respectively, for the reconstruction of images with good quality. The proposed multi stage vector quantization technique is faster when compared to existing techniques and yields better trade-off between image quality and block size for encoding.     

Adaptive hierarchical vector quantization for image coding

In this paper an adaptive hierarchical algorithm of vector quantization for image coding is proposed. First the basic codebook is generated adaptively, then the codes are coded into higher-level codes by creating an index codebook using the redundance presented in the codes. This hierarchical scheme lowers the bit rate significantly and causes little more computation and no more distortion than the single-layer adaptive VQ algorithm does which is used to create the basic codebook.     

自适应多层次矢量量化方法

我们在本文中提出了一个矢量量化的自适应多层次算法。首先生成一个基本码本和码字序列，然后码字序列再被进一步编成一个多层次的码，该算法利用了图象上更大范围的结构特点和相关性，减少了单层矢量量化方法得到的码的冗余信息，从而有效地提高了压缩比，同时却没有增加误差，也未增加很多计算量。     

Adaptive Vector Quantization for Speech Spectrum Coding

We address the problem of speech compression at very low rates, with the short-term spectrum compressed to less than 20 bits per frame. Current techniques apply structured vector quantization (VQ) to the short-term synthesis filter coefficients to achieve rates of the order of 24 to 26 bits per frame. In this paper we show that temporal correlations in the VQ index stream can be introduced by dynamic codebook ordering, and that these correlations can be exploited by lossless coding approaches to reduce the number of bits per frame of the VQ scheme. The use of lossless coding ensures that no additional distortion is introduced, unlike other interframe techniques. We then detail two constructive algorithms which are able to exploit this redundancy. The first method is a delayed-decision approach, which dynamically adapts the VQ codebook to allow for efficient entropy coding of the index stream. The second is based on a vector subcodebook approach and does not incur any additional delay. Experimental results are presented for both methods to validate the approach.     

A novel encoding algorithm for vector quantization using transformed codebook

In this paper, a novel encoding algorithm for vector quantization is presented. Our method uses a set of transformed codewords and partial distortion rejection to determine the reproduction vector of an input vector. Experimental results show that our algorithm is superior to other methods in terms of the computing time and number of distance calculations. Compared with available approaches, our method can reduce the computing time and number of distance calculations significantly. Compared with the available best method of reducing the number of distance computations, our approach can reduce the number of distance calculations by 32.3-67.1%. Compared with the best encoding algorithm for vector quantization, our method can also further reduce the computing time by 19.7-23.9%. The performance of our method is better when a larger codebook is used and is weakly correlated to codebook size.     

矢量量化的初始码书算法

矢量量化的初始码书设计是很重要的,影响或决定着其后码书形成算法的迭代次数和最终的码书质量。针对原有的初始码书算法在性能上随机性强与信源匹配程度不高的问题,提出一种对于训练矢量实施基于分量的和值排序,然后做分离平均的初始码书形成算法。算法使用了矢量的特征量,脱离了对于图像结构因数的依赖,能产生鲁棒性较好的初始码书。实验证明了该方法的有效性,与LBG算法结合可进一步提高码书质量。     

矢量量化压缩算法在高光谱影像上的研究实现

针对高光谱影像光谱维的数据量大、传统影像压缩方法不易于保存光谱内信息的特点,对矢量量化数据压缩方法中码书设计和码字搜索两个关键技术进行详细地研究,提出针对高光谱影像压缩的改进方法,并在此基础上实现了对高光谱影像的矢量量化压缩算法。最后通过对不同波段组合的AVIRIS的高光谱数据的实验,从压缩后的压缩率、速率和失真率等方面进行观察和对比,证明矢量量化压缩算法对高光谱影像具有显著的压缩效果。