PDVQ图像编码系统的算法研究

PDVQ图像编码系统首先将码书进行方向性分类,把每类方向性码书中的码字按码字和值进行升序排列,并根据EBNNS算法将码书分块。编码时,先根据输入图像块的相关性进行PDVQ编码,然后分析输入图像块的方向性来选择相应的分类子码书,在该子码书中根据输入图像块的和值确定码字搜索范围,最后在确定的搜索范围内搜索最匹配码字。仿真结果表明,该系统集合了动态图像块划分（PDVQ）、基于方向性分类编码和等和值块扩展最近邻码字搜索（EBNNS）三种算法的优点,在保证重建图像质量前提下,缩短了编码时间,并提高了压缩比。     

基于主分量分析的矢量量化数字水印算法

针对矢量量化过程中码书训练复杂的缺点,基于主分量分析方法对图像进行降维,根据降维后各主分量熵和标准差的差异性对其进行分类,采用非均匀矢量量化方法生成码书。在水印嵌入过程中,将水印图像嵌入降维后主分量能量适中的码书中以提高水印图像质量,利用EENNS算法代替完全搜索算法缩短编码时间。实验结果表明,该算法在提高码书质量的同时,能有效减少码书训练时间,对JPEG压缩、剪切、旋转等图像攻击也具有较强的鲁棒性。     

一种基于图像矢量量化压缩的数据隐藏方法

根据码书中所有码字之间的最近邻关系,采用最近邻方法标记码书使得所有码字与其最近邻码字的标记值不同。在此基础上,运用I/矩阵运算可在矢量量化压缩图像的每n=2m个码字索引中有选择地最多修改m2」个从而嵌入m比特数据。计算机上的仿真实验结果表明：该方法不仅具有较大的嵌入容量,而且具有较好的隐蔽性,可用于矢量量化压缩图像中的信息隐藏。     

码书排序对快速码字搜索算法性能影响的分析

矢量量化快速码字搜索算法中,为了有效地减小搜索范围,必须对原始码书按一定的准则进行重新排序。对现存的两类快速码字搜索算法进行了总结,其中一类是码书按1维顺序关系排序,另一类是码书按2维相邻关系排序。通过实验给出了两类算法的搜索范围和编码时间,并进行了比较和分析,进而提出了在实际编码时如何更好地使用这两种排序关系的准则。     

均值与矢量量化复合快速图像编码算法

矢量量化是一种有效的数据压缩技术,由于其算法简单,具有较高的压缩率,因而被广泛应用于数据压缩编码领域。通过对图像块灰度特征的研究,根据图像的平滑与否,提出了对图像进行均值和矢量量化复合编码算法,该算法对平滑图像块采用均值编码,对非平滑块采用矢量量化编码。这不仅节省了平滑码字的存储空间,提高了码书存储效率,并且编码速度大大提高。同时采用码字旋转反色（2R）压缩算法将码书的存储容量减少到1/8,并结合最近邻块扩展搜索算法（EBNNS）对搜索算法进行优化。在保证图像画质的前提下,整个系统的图像编码速度比全搜索的普通矢量量化平均提高约7.7倍。     

基于连续及非连续块编码的测试数据压缩方法

提出了一种新的基于连续及非连续长度块编码的测试数据压缩方法,该方案从提高码字利用率的目的出发,利用定长的二进制码字表示连续长度块的长度信息,同时,将连续位长度不足的序列按一定的策略划为非连续块,并且不对其进行编码,故有效地避免了用长码字替换短游程序列的情况。该方案的编码规则减少了使用前、后缀形式编码的复杂性,所以其编码及解码过程简单,同时具有简单的通讯协议。对ISCAS-89标准电路Mintest集的压缩结果表明,提出的方案较FDR码和Golomb码都具有更好的压缩效率。     

等和值块扩展最近邻矢量量化码字搜索算法

论文提出一种等和值块扩展最近邻矢量量化码字搜索算法。该算法将码书按和值大小排序分块,并将每一块中间或中间附近的码字的和值作为本码书块的特征和值。编码时,查找与输入矢量和值距离最近的码书块并作为初始匹配码书块。然后在该码书块附近上下扩展搜索相邻码书块中距输入矢量最近的码字。该算法具有无复杂运算的特点,易于VLSI技术实现。仿真结果表明,该算法是一种有效的码字搜索算法。     

改进的SOFM算法及其在低延迟语音编码中的应用

根据低延迟语音编码算法训练码书的尺寸和码字维数的特点,提出了一种改进的自组织特征映射（SOFM）神经网络的码书设计方法。对输入训练矢量以及连接权矢量进行归一化,为降低计算量和提高码书训练质量,采用快速的网络学习决定获胜的神经元并对网络权值分阶段进行自适应调整,最后应用于低延迟语音编码中。实验表明,与传统LBG算法比较,采用SOFM神经网络训练的码书其合成语音的主、客观质量均有较大提高。     

基于折叠方法的数据压缩方法实现

该文提出了一种基于折叠关系压缩方案,该方案是利用折叠技术,将SOC芯片中芯核的测试数据整体进行折叠关系的判断,并且能够根据是否存在折叠关系把原测试数据分为两段,在此基础之上并分别对有折叠关系的测试数据进行折叠压缩,对没有折叠关系的测试数据使用相容压缩。目前,减少测试应用时间和测试数据容量是测试领域的努力方向。该文提出的这种方法可以有效的减少存储容量和降低测试时间从而有效的降低了测试成本。与类似的纯编码压缩方法相比,如:Golomb码,统计码,基于字典的编码等压缩方法,其压缩效果更为显著。     

涡流栅位移传感器绝对定位可靠性算法

为了提高涡流栅位移传感器绝对定位的可靠性,利用编码码道信号之间互补关系,提出了一种新型的编码码道码字识别算法,即三类码字识别算法,该算法可将编码码道归一化误差限提高到±0.1。在此基础上,对归一化误差曲线经过有选择性的最小二乘法拟合,可将归一化误差进一步降低至±0.05。实验证明该方法可以满足码字识别算法的要求,采用该技术的涡流栅位移传感器确保了绝对定位的可靠性,提高了传感器的稳定性。     

A novel approach for fast codebook re-quantization

In this paper, we present a fast codebook re-quantization algorithm (FCRA) using codewords of a codebook being re-quantized as the training vectors to generate the re-quantized codebook. Our method is different from the available approach, which uses the original training set to generate a re-quantized codebook. Compared to the traditional approach, our method can reduce the computing time dramatically, since the number of codewords of a codebook being re-quantized is usually much smaller than the number of original training vectors. Our method first classifies codewords of a re-quantized codebook into static and active groups. This approach uses the information of codeword displacements between successive partitions to reject impossible candidates in the partition process of codebook re-quantization. By implementing a fast search algorithm used for vector quantization encoding (MFAUPI) in the partition step of FCRA, the computational complexity of codebook re-quantization can be further reduced significantly. Using MFAUPI, the computing time of FCRA can be reduced by a factor of 1.55–3.78. Compared with the available approach OARC (optimization algorithm for re-quantization codebook), our proposed method can reduce the codebook re-quantization time by a factor of about 8005 using a training set of six real images. This reduction factor is increased when the re-quantized codebook size and/or training set size are increased. It is noted that our proposed algorithm can generate the same re-quantized codebook as that produced by the OARC.     

Hadamard transform based fast codeword search algorithm for high-dimensional VQ encoding

An efficient nearest neighbor codeword search algorithm for vector quantization based on the Hadamard transform is presented in this paper. Four elimination criteria are derived from two important inequalities based on three characteristic values in the Hadamard transform domain. Before the encoding process, the Hadamard transform is performed on all the codewords in the codebook and then the transformed codewords are sorted in the ascending order of their first elements. During the encoding process, firstly the Hadamard transform is applied to the input vector and its characteristic values are calculated; secondly, the codeword search is initialized with the codeword whose Hadamard-transformed first element is nearest to that of the input vector; and finally the closest codeword is found by an up-and-down search procedure using the four elimination criteria. Experimental results demonstrate that the proposed algorithm is much more efficient than the most existing nearest neighbor codeword search algorithms in the case of problems of high dimensionality.     

H.264/AVC视频流完整性认证的脆弱水印算法

提出一种基于脆弱水印的视频流完整性认证方法,在H.264压缩比特流中,首先根据I_Slice 中4×4块的编码模式生成认证码,然后通过调制某些运动子块的VLC码字将其嵌入B_Slice和P_Slice的视频流中。这种调制是基于VLC码字和待嵌入比特之间的映射规则进行的。映射后的码字与原码字具有很好的相似性,即码字长度不变,码字表示的编码元素值相似。该算法可以实现水印的快速提取,满足视频实时处理的要求。实验仿真结果表明,本算法具有较小的视频失真,并能有效地对视频帧进行完整性认证。     

一种新颖的基于哈德码变换的码字搜索算法

针对图像矢量量化编码的复杂性,提出了一种新颖的快速最近邻码字搜索算法。该算法首先计算出每个码字和输入矢量的哈德码变换,然后为输入矢量选取范数距离最近的初始匹配码字,利用多控制点的三角不等式和两条有效的码字排除准则,把不匹配的码字排除,最后选取与输入矢量最匹配的码字。实验结果表明,新算法相比于其他算法,在保证编码质量的前提下,码字搜索时间和计算量均有了明显降低。     

基于空时编码和干扰对齐的干扰消除方法

对于每个用户配置4根天线的X信道,提出了一种基于空时编码和干扰对齐的干扰消除方法。设计了4×4的速率为2的空时码字,并且在码字中引入零向量。首先通过干扰对齐将每个接收端接收到的非期望接收码字对齐,然后对接收信号进行线性操作以消除非期望接收的码字,最后对接收信号进行非线性运算使得期望接收的码字互不干扰。所提方案的自由度为16/3,分集增益为8。仿真结果显示,所提方案的可靠性优于相同场景中的已有方案。     

Accelerating VQ-based codeword search on the basis of partial search strategy

In this paper, a technique for accelerating the search on VQ-based codeword search is proposed. With our approach, all the pixel blocks of vector representation in an image picture could be encoded efficiently into their corresponding indices, and be associated with the closest codeword in the pre-generated codebook. The technique adopted in our scheme is inspired by the concept of space partition of the initial codebook. It is accomplished in a manner that the search range for the image block is significantly reduced. There is a key-codebook comprised of numerous key-codewords, and with a smaller book size, which is generated from the given codewords during system initialization. Any image block is then directed to look for the closest key-codeword in the key-codebook. Ultimately, the best-match codeword is checked out according to the relation between the closest key-codeword and the ‘genuine’ codewords in the given codebook. This short-time achievement is obtained because of the considerable book size reduction. A flexible radius, spread by a key-codeword is imposed in our elaborated algorithm to attain the most precise hit ratio estimation. The experiments show that our scheme is at least two and a half times faster than that of a full search in VQ implementation. Moreover, the strategy we proposed is also compatible with the search algorithms in finding the closest codeword, and the high quality of image display remained the same.     

Adaptive embedding techniques for VQ-compressed images

An embedding algorithm, which can adaptively embed a binary message into a VQ-compressed image, is proposed in this paper. The proposed algorithm is divided into three phases. In the codeword grouping phase, a new group of codewords is initiated by the two most similar codewords which do not belong to any group. For each codeword which does not belong to any group, if the codeword is similar to all of the codewords in the group, it will be added to the group. In the embedding phase, each codeword in a group will be assigned to embed a certain sub-message whose length is determined by the number of codewords in the group. The more codewords a group has, the higher the embedding capacity of a codeword in the group will be. In the extracting phase, given a codeword and the number of codewords in the group to which the codeword belongs, the embedded message can be extracted from the codeword by simply determining the order of the codeword in the group. Experimental results show that the proposed algorithm performs better than previous algorithms with regard to embedding capacity and image quality. For the test images, when the embedding capacity is less than 5 bits per codeword index, the difference of the PSNR values between the stego-image and its VQ-compressed cover image will be no more than 5 dB on average.