基于自适应搜索范围及顺序的快速矢量量化编码算法

本文提出了一种新颖的快速矢量量化编码算法.该算法在编码前预先计算每个码字的四个特征量,然后根据各特征量的升序排列分别对码字进行排序以生成四个排序码书.在编码过程中,对于不同的输入矢量,自适应产生不同的动态码字搜索范围及顺序而排除大部分码字.测试结果表明,本文算法只需搜索3%到8%码字而获得与穷尽搜索算法相近的编码质量,实际编码时间减少约93%.     

哈德码变换域等均值等方差最近邻矢量量化码字搜索算法

本文提出一种基于哈德码变换的等均值等方差最近邻(HTEENNS)快速矢量量化码字搜索算法.在编码前,该算法预先计算每个码字的哈德码变换,然后根据各码字哈德码变换的第一维系数大小的升序排列对码字进行排序.在编码过程中,首先计算输入矢量的哈德码变换和方差,然后选取与输入矢量哈德码变换的第一维系数最近的码字作为初始匹配码字,然后利用两条有效的删除准则在该码字附近进行上下搜索与输入矢量最近的码字.测试结果表明,本文算法比等均值最近邻搜索算法(ENNS)、等均值等方差最近邻搜索(EENNS)算法和哈德码变换域部分失真搜索算法等算法有效得多.     

粒子对算法在图像矢量量化中的应用

本文给出了一种新的图像矢量量化码书的优化设计方法——粒子对算法.在传统粒子群优化(Particle Swarm Optimization,PSO)算法的基础上,用两个粒子构成了群体规模较小的粒子对,在码书空间中搜索最佳码书.在每次迭代运算中,粒子对按先后顺序执行PSO算法中的速度更新、位置更新操作和标准LBG算法,并用误差较大的训练矢量代替越界的码字.此算法避免粒子陷入局部最优码书,较准确地记录和估计每个码字的最佳移动方向和历史路径,在训练矢量密集区域和稀疏区域合理地分配码字,从而使整体码书向全局最优解靠近.实验结果表明,本算法始终稳定地取得显著优于FKM、FRLVQ、FRLVQ-FVQ算法的性能,较好地解决了矢量量化中初始码书影响优化结果的问题,且在计算时间和收敛速度方面有相当的优势.     

基于FPGA的EBNNS矢量量化码字搜索算法的电路设计

等和值块扩展最近邻搜索算法(EBNNS)是一种快速矢量量化码字搜索算法,该算法首先将码书按和值大小排序分块,编码时查找与输入矢量和值距离最近的码书块中间码字,并将它作为初始匹配码字.然后在该码字附近上下扩展搜索相邻码字中距输入矢量最近的码字,最后将搜索到的最匹配码字在码书中的地址输出.同时本文对该算法进行了FPGA设计.设计时采用串并结合和流水线结构,折中考虑了硬件面积和速度.结果表明针对所用FPGA器件Xilinx xc2v1000,整个系统最大时钟频率可达88.36MHz,图像处理速度约为2.2 MPixel/s.     

矢量量化有效排查快速编码算法

研究了一种基于均方误差（MSE）测度的矢量量化快速编码算法,算法利用小波变换的特点合理地构造矢量。结合非线性插补矢量量化技术、矢量和值差法和部分失真排除法,在搜索编码过程中,有效排除部分候选码字。实验结果表明,相对于穷尽搜索方法,计算量有明显降低,计算时间减少显著。     

一种改进的基于Hadamard变换的快速码字搜索算法

提出了一种矢量量化码字搜索的快速算法。该算法是在Hadamard变换域内进行的。匹配码字的判决过程首先根据Chebyshev误差测度,在码书中找出一定数量Chebyshev误差最小的码字;然后运用部分失真搜索算法(PDS),在上述码字中找出其中最匹配的码字。从理论分析和模拟实验结果表明,该算法在保证较好的性能指标和视觉效果前提下,明显减少码字搜索时间。     

一种矢量量化编码的加速算法

本文提出了一种基于最小均方误差测度的快速矢量量化编码算法。在进行均方测度计算之前，通过一组距离测度的不等式判据和预排序的码书，排了作大部分候选码字。     

等均值等范数最近邻矢量量化码字搜索算法

本文提出了一种等均值等范数最近邻(EENNS)矢量量化码字搜索算法.在编码前,该算法预先计算每个码字的均值和范数,然后根据均值大小的升序排列对码字进行排序.在编码过程中,首先选取与输入矢量均值最近的码字作为初始匹配码字,然后利用两条有效的删除准则在该码字附近进行上下搜索与输入矢量最近的码字.测试结果表明,本文算法比等均值最近邻搜索算法(ENNS)和最近提出的范数排序搜索(NOS)算法有效得多.     

基于Hadamard变换和自适应顺序搜索的码字快速搜索算法

提出了一种Hadamard域中改进的快速码字搜索算法.在已离线按照码字第一维分量的大小进行了排序的码书中,首先找出与输入矢量第一维分量最接近的L个初始候选码字,求出对应的L个Chebyshev距离,接着按自适应的方法在这L个码字之外进行上下搜索,并用新找到的具有更小Chebyshev距离的码字来更新这L个候选码字,以便得到全体码书中L个具有最小Chebyshev距离的最终候选码字.最后用PDS算法在这L个最终候选码字中找出Euclidean距离最小的码字作为最佳匹配码字.实验表明文中算法相比本文算法在保证PSNR性能无任何下降的前提下,明显减少了算法的计算量,有效地提高了编码速度.     

一种改进的基于Hadamard域的码书设计算法

基于Hadamard变换和K-means理论,针对Chen的初始码书设计算法的随机性较强和峰值信噪比(PSNR)不高这两个缺点,提出了一种改进的码书设计算法。本算法利用统计特征量的分类平均法生成初始码书,然后提高求质心的频率,每当一个训练矢量被分类到胞腔时,就求出相应胞腔的质心来代替原有的码字。该算法结合LBG算法的优点,调整后的码字代表了整个胞腔的特性,加速了码书的收敛速度,提升了码书的性能。仿真实验结果表明,较Chen的算法图像效果,即峰值信噪比(PSNR),平均提高了0.5 dB,在迭代次数较小时甚至达0.9 dB。     

A fast finite-state algorithm for vector quantizer design

The Linde-Buzo-Gray (LBG) algorithm is usually used to design a codebook for encoding images in vector quantization. In each iteration of this algorithm, one must search the full codebook in order to assign the training vectors to their corresponding codewords. Therefore, the LBG algorithm needs a large computation effort to obtain a good codebook from the training set. The authors propose a finite-state LBG (FSLBG) algorithm for reducing the computation time. Instead of searching the entire codebook, they search only those codewords that are close to the codeword for a training vector in the previous iteration. In general, the number of these possible codewords can be made very small without sacrificing performance. By only searching a small part of the codebook, the computation time is reduced. In experiments, the performance of the FSLBG algorithm in terms of signal-to-noise ratio is very close to that of the LBG algorithm. However, the computation time of the FSLBG algorithm is about 10% of the time required by the LBG algorithm     

Fast closest codeword search algorithms for vector quantisation

One of the most serious problems for vector quantisation is the high computational complexity of searching for the closest codeword in the codebook design and encoding phases. The authors present a fast algorithm to search for the closest codeword. The proposed algorithm uses two significant features of a vector, mean value and variance, to reject many unlikely codewords and saves a great deal of computation time. Since the proposed algorithm rejects those codewords that are impossible to be the closest codeword, this algorithm introduces no extra distortion than conventional full search method. The results obtained confirm the effectiveness of the proposed algorithm     

Peano scanning based fast encoding algorithm for vectorquantisation

An effective method for encoding image-input blocks in vector quantisation (VQ) is proposed. For each codeword in the codebook, a group of Peano scannings of selected feature vectors is computed. Each Peano scanning acts as a transform from a higher dimension to one dimension, while preserving neighbourhood adjacency. An ordered list of the Peano scannings and their link to the codebook is stored. Coding is conducted by restricting the search to two windows of codewords with the closest Peano scannings to that of the input block. Each window centre is found in logarithmic time proportional to the codebook size. The number of codewords to be searched is fixed, and is determined by some additional distortion that is acceptable over exhaustive search methods. Coded images show no significant degradation, while maintaining considerable constant search-time savings over exhaustive search methods. The algorithm can be used with other fast full-search equivalent methods, and can use savings from other methods in searching within the windows as well     

An efficient encoding algorithm for vector quantization based on subvector technique

In this paper, a new and fast encoding algorithm for vector quantization is presented. This algorithm makes full use of two characteristics of a vector: the sum and the variance. A vector is separated into two subvectors: one is composed of the first half of vector components and the other consists of the remaining vector components. Three inequalities based on the sums and variances of a vector and its two subvectors components are introduced to reject those codewords that are impossible to be the nearest codeword, thereby saving a great deal of computational time, while introducing no extra distortion compared to the conventional full search algorithm. The simulation results show that the proposed algorithm is faster than the equal-average nearest neighbor search (ENNS), the improved ENNS, the equal-average equal-variance nearest neighbor search (EENNS) and the improved EENNS algorithms. Comparing with the improved EENNS algorithm, the proposed algorithm reduces the computational time and the number of distortion calculations by 2.4% to 6% and 20.5% to 26.8%, respectively. The average improvements of the computational time and the number of distortion calculations are 4% and 24.6% for the codebook sizes of 128 to 1024, respectively.     

VQ域信息隐藏检测算法

随着信息隐藏技术的发展，基于矢量量化（VQ）的信息隐藏的方法逐渐增多．本文提出了一种VQ域隐藏信息检测方法，首先通过全局搜索在已经解码的无压缩图像中恢复量化码书，然后应用聚类分析的思想将矢量空间中相近的码字归类以判断是否存在隐藏信息，最后根据各个码字的出现概率估计嵌入信息的长度．实验表明，本文提出的算法对隐藏信息的存在性有很高的识别率，对信息量的估计也比较准确．     

A vector quantizer with minimum visible distortion

A method for designing codebooks for vector quantization (VQ) based on minimum error visibility in a reconstructed picture is described. The method uses objective measurements to define visibility for the picture being coded. The proposed VQ is switched type, i.e., the codebook is divided into subcodebooks, each of which is related to a given subrange of error visibility. Codebook optimization is carried out on the basis of a particular definition of visible distortion of the reconstructed image. Subjective judgment of the test results, carried out at 0.5 b/pel bit rate, indicates that the proposed VQ enables low-distortion images to be reconstructed even when subcodebooks with a small number of codewords are used, thus reducing the codebook search time to about 10% of that required by a fixed VQ (both inside and outside the training set)     

一种新的低功耗的图象编/解码算法

该文提出了一种改进的低功耗并且只需少量存储空间的图像,视频编码方案,它是基于矢量量化的,K.Masselos等人1998年提出的方案使用了一个小的基本码书,然后通过对基本码书中的每一码字进行八种对称变换,从而把小的基本码书扩展成为一个大的虚码书,补偿了因为码书的尺寸太小而引起的图象质量下降,因为它的基本码书小,从而对存储器的访问次数也就少,那么功耗也就小。同原方案相比,本文做出了如下两个改进:一是基本码书中存放的是码字的经过量化的DCT系数,寻找最佳匹配码字也放在DCT域内进行,二是改进了K.Masselos文中的基本码书的设计方案,消除了码书中存在的重复。改进方案具有计算量更小,操作更灵活的特点。