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.     

一种新的基于子空间三角不等式快速搜索的参考矢量选择方法

提出了一种新的参考矢量选择方法.编码前,首先计算所有码字的哈德码变换,按照第一维系数的大小对码字进行升序排序,将排序后的码字平均分为四段,选择每一段中间位置的码字作为该段的参考矢量,从而增加了子空间三角不等式删除准则的删除能力,有效减小了搜索空间,加快了搜索速度.仿真实验表明,对不同复杂程度的测试图像,算法均快于其他搜索算法.     

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.     

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%.     

一种基于不等式的矢量量化快速码字搜索算法

传统矢量量化编码算法码字搜索范围较大,编码时间较长.文章提出一种基于不等式的矢量量化快速码字搜索算法.该算法将方差不等式和三角不等式引入范数排序算法(NOS),有效减小了码字搜索范围.实验结果表明,重构图像峰值信噪比(PSNR)相同时,该算法编码时间较低.     

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.     

基于特征值排序的部分失真快速码字搜索算法

提出了一种矢量量化快速码字搜索算法．该算法在编码前预先计算每个码字的特征值并按顺序排列；在编码时，根据每个输入矢量的特征值来确定码字搜索顺序。同时限定相应的搜索范围及利用有效的码字删除准则，从而大大提高了编码速度．实验表明，该算法只需要穷尽算法2％-4％的编码时间就可以获得与之较为接近的编码质量，编码速度与ASRSS算法及MEENNS算法相比也有明显提高。     

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。     

一种改进的矢量量化码字搜索算法

该文利用图像矢量的平均值和方差,结合了最近邻域搜索算法,构造了一种新的快速矢量量化编码算法。将一个输入矢量分为两个子矢量,分别计算原始矢量、两个子矢量的和以及方差值,利用在这些数值基础上建立的一组三角不等式来排除不可能的码字。仿真结果表明新算法在所需时间和计算复杂度方面优于改进的EENNS算法,为矢量量化算法的研究提供了一种新的思路。     

一种矢量量化码书搜索的快速算法

本文提出了一种采用均方误差(MSE)测度的矢量量化码书搜索的快速算法.该算法在码书设计的每次迭代前预先计算各码字的和值(一个矢量各分量的和)并保存在码书中.在迭代过程中,利用输入矢量的和值、各码字的和值以及均方误差三者之间的各种特性排除大部分候选码字而免去许多均方误差计算.测试结果表明,相对于穷尽搜索方法,计算量得到明显的降低,计算时间减少约90%,同时只需要很少的预先计算量和额外存储量.     

Efficient codeword search algorithm based on Hadamard transform

A new fast codeword search algorithm for image vector quantisation (VQ) is introduced. This algorithm performs a fast codeword search in the Hadamard transform (HT) domain using the partial distance search (PDS) technique. Experimental results show that the algorithm needs only 2-3% of the distortion calculations of the exhaustive search method     

Two fast nearest neighbor searching algorithms for image vectorquantization

In this paper, two efficient codebook searching algorithms for vector quantization (VQ) are presented. The first fast search algorithm utilizes the compactness property of signal energy on transform domain and the geometrical relations between the input vector and every codevector to eliminate those codevectors that have no chance to be the closest codeword of the input vector. It achieves a full search equivalent performance. As compared with other fast methods of the same kind, this algorithm requires the fewest multiplications and the least total times of distortion measurements. Then, a suboptimal searching method, which sacrifices the reconstructed signal quality to speed up the search of nearest neighbor, is presented. This algorithm performs the search process on predefined small subcodebooks instead of the whole codebook for the closest codevector. Experimental results show that this method not only needs less CPU time to encode an image but also encounters less loss of reconstructed signal quality than tree-structured VQ does     

Fast codeword search algorithm for real-time codebook generation inadaptive VQ

Adaptive vector quantisation is used in image sequence coding where the code-book is updated continuously to keep track with the changing source statistics. Hence, for real-time video coding applications, both the processes of quantising the input vectors and updating the codebook are required to be fast. Since the nearest codeword search is involved in both these processes, a fast codeword search algorithm can make the coding process time efficient. The authors describe a proposed codeword search algorithm with reduced search space. The algorithm uses the mean value and the sum of the absolute differences as the two criteria to reject unlikely codewords, thereby saving a great deal of computational time, while introducing no more distortion than the conventional full search algorithm. Simulation results obtained confirm the effectiveness of the proposed algorithm in terms of computational complexity     

A fast search algorithm for vector quantization usingL2-norm pyramid of codewords

Vector quantization for image compression requires expensive encoding time to find the closest codeword to the input vector. This paper presents a fast algorithm to speed up the closest codeword search process in vector quantization encoding. By using an appropriate topological structure of the codebook, we first derive a condition to eliminate unnecessary matching operations from the search procedure. Then, based on this elimination condition, a fast search algorithm is suggested. Simulation results show that with little preprocessing and memory cost, the proposed search algorithm significantly reduces the encoding complexity while maintaining the same encoding quality as that of the full search algorithm. It is also found that the proposed algorithm outperforms the existing search algorithms.     

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.