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。     

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。     

基于主分量分析的相关矢量量化编码算法

在编码前,首先计算码书中所有码字在主轴上的投影值,然后按照这些投影值从小到大对码字进行排序;在编码过程中,利用邻近图像块的高度相关性和当前输人矢量在主轴上的投影值共同确定相应的码字搜索范围．实验结果表明,与传统穷尽搜索矢量量化编码法相比,虽然文中算法的编码质量略有下降,但编码速度和压缩效率都有了显著的提高．     

基于方向性的VQ分类编码算法

该文提出了一种基于图像块方向性的分类码书的生成方法,并提出与之相适应的分类编码算法。实验结果表明:用分类码书配合分类编码算法对图像进行编码可以大幅提高图像编码速度。在PSNR仅降低1.8%的情况下,编码速度平均提高38.4%。最高可以提高45.8%。     

基于动态SMVQ的图像压缩算法

SMVQ利用相邻图像块之间的相关性来提高编码图像质量。然而，传统的SMVQ当图像边缘位于当前编码图像块右下角时会出现编码误差，为此，研究并提出了一种改进的SMVQ，新算法利用图像所有图像块之间的相关性对当前编码图像块进行预测。对每一个输入图像块，利用边匹配方法从已编码数据中寻找最近块，并用其对输入图像块进行预测，或产生动态码书。然后利用最近块、动态码书或总码书对输入数据进行编码，扩展了编码搜索范围，提高了图像质量。实验结果表明新算法大大降低了比特速率，而且编码图像有较好的视觉效果。     

基于规范块半范数的快速分形编码算法

分形图像编码通常需要较长的时间,编码时间主要花费在一个海量码本中搜索每个输入子块的最佳匹配块。在提出的叉迹算法的基础上,提出一种快速编码算法。它主要利用一种新定义的特征——规范块半范数,把搜索范围限制在初始匹配块（在半范数意义下与输入子块最接近的码块）的邻域内。实验显示,与叉迹算法比较,该算法在编码时间和图像质量方面都取得了更好的效果。     

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。     

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。     

低复杂度视频编码算法研究与实现

研究帧内预测模式的选择。根据图像自身的特点,对其宏块的像素值进行平坦性分析,确定较小的模式搜索范围;再根据宏块的方向性,排除小概率预测模式,从而大大缩小视频帧内编码的搜索模式范围。实验表明,改进算法提高了编码效率,平均节省约52.40%的模式预测时间,在保证视频图像质量的同时,实现低复杂度的视频编码新算法。     

低复杂度视频编码算法研究与实现

研究帧内预测模式的选择。根据图像自身的特点,对其宏块的像素值进行平坦性分析,确定较小的模式搜索范围;再根据宏块的方向性,排除小概率预测模式,从而大大缩小视频帧内编码的搜索模式范围。实验表明,改进算法提高了编码效率,平均节省约52.40%的模式预测时间,在保证视频图像质量的同时,实现低复杂度的视频编码新算法。     

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。     

矢量量化中码书旋转压缩的研究

普通码书中的码字之间在不同的方向上具有很大的相关性,存在大量的数据冗余。提出了将码书中的码字旋转压缩的理论。该理论是将各个码字按四个方向垂直旋转后进行相似性检查。如果旋转后的码字其中一个方向上与前面的码字存在相似,则将该码字删除,从而达到压缩的目的。编码时将压缩后的码书旋转恢复后进行编码,从而大幅降低了需要存储的码字数量。同时给出了一种将现有1 024阶16维码书旋转压缩成256阶16维的方法,并对该方法得到的码书性能进行了仿真验证。实验结果表明使用压缩后的码书在硬件实现时与普通的矢量量化码书相比减少了75%的存储空间和输入带宽,而PSNR平均只降低0.28 dB。     

Constrained-storage multistage vector quantization based on genetic algorithms

Multistage vector quantization (MSVQ) and their variants have been recently proposed. Before MSVQ is designed, the user must artificially determine the number of codewords in each VQ stage. However, the users usually have no idea regarding the number of codewords in each VQ stage, and thus doubt whether the resulting MSVQ is optimal. This paper proposes the genetic design (GD) algorithm to design the MSVQ. The GD algorithm can automatically find the number of codewords to optimize each VQ stage according to the rate–distortion performance. Thus, the MSVQ based on the GD algorithm, namely MSVQ(GD), is proposed here. Furthermore, using a sharing codebook (SC) can further reduce the storage size of MSVQ. Combining numerous similar codewords in the VQ stages of MSVQ produces the codewords of the sharing codebook. This paper proposes the genetic merge (GM) algorithm to design the SC of MSVQ. Therefore, the constrained-storage MSVQ using a SC, namely CSMSVQ, is proposed and outperforms other MSVQs in the experiments presented here.     

使用2－范数匹配的快速分形图像编码算法

尽管分形图像压缩在高压缩比时具有高的重建图像质量,但由于它编码过程耗时而未能在图像压缩领域广泛应用。为了提高分形图像编码过程的速度,提出了基于2－范数匹配的快速分形图像编码算法,该算法先把码书里的码块按2－范数大小赋序,然后对每个输入Range块,在赋序码书中寻找与它的2－范数最接近的码块,进而在这个码块的领域内搜索Range块的最佳匹配块。计算机仿真结果显示,在不影响基本分形图像编码算法解码图像质量的情况下,该方案大大加快了它的编码速度。     

VQ-based watermarking scheme with genetic codebook partition

To enhance the traditional vector quantisation (VQ) system by adding the watermarking ability, a digital image watermarking scheme, which modifies the VQ indices to carry watermark bits, is presented. This scheme partitions the main codebook into two sub-codebooks by referring to the user-key. Then, for each input vector of the cover image, a sub-codebook is selected according to the watermark bit to be embedding. The traditional VQ coding procedure is then done using the sub-codebook for the vector. Furthermore, to improve the performance of the scheme, a genetic codebook partition (GCP) procedure, which employs the genetic algorithm (GA) to find a better way to split the codebook, is proposed. It is demonstrated that the proposed methods provide faster encoding time, better imperceptibility, stronger robustness under some common attacks, and easier implementation than some related VQ-based watermarking schemes proposed in the literature.     

An improved search algorithm for vector quantization using mean pyramid structure

Vector quantization (VQ) is a well-known data compression technique. In the codebook design phase as well as the encoding phase, given a block represented as a vector, searching the closest codeword in the codebook is a time-consuming task. Based on the mean pyramid structure and the range search approach, an improved search algorithm for VQ is presented in this paper. Conceptually, the proposed algorithm has the bandpass filter effect. Each time, using the derived formula, the search range becomes narrower due to the elimination of some portion of the previous search range. This reduces search times and improves the previous result by Lee and Chen (A fast search algorithm for vector quantization using mean pyramids of codewords. IEEE Trans. Commun. 43(2/3/4), (1995) 1697–1702). Some experimental results demonstrate the computational advantage of the proposed algorithm.     

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.