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。     

基于小波变换的分类量化图像编码算法

针对标量量化压缩比小而向量量化压缩速度慢、图像复原效果不理想等弱点，提出了基于小波变换的分类量化图像编码算法（简称“分类量化编码”）。该算法基本思想为：首先将小波变换后的图像高频子带划分为局部快；然后利用文中给出的相对距离最近之值选择方法，依据纹理复杂度和重要性程度将这些局部块划分为4类（平坦、过渡、弱纹理和强纹理）；最后对平坦局部块进行向量量化编码，对强纹理局部进行标量量化编码。实验结果表明：该图像压缩算法在压缩速度、图像复原效果、压缩比等方面明显优于零树小波编码和JPEG方法。     

一种快速DCT图像压缩算法的研究

主要对JPEG图像压缩标准中的DCT变换和量化部分提出了一些改进意见。其中 ,DCT变换部分采用查表法代替常规DCT算法 ,大大提高了其运算速度 ;量化部分用基于简单有效的“二重测试算法”的矢量量化取代了一般的欧氏距离矢量搜索算法 ,明显加快了码书的搜索速度。     

基于非局部低秩和自适应量化约束先验的HEVC后处理算法

经高效视频编解码标准HEVC压缩后的视频在高压缩比、低码率的情况下存在明显的压缩效应。针对该问题,提出了一种基于非局部低秩(Non-local Low-rank,NLLR)和自适应量化约束(Adaptive Quantization Constraint,AQC)先验的HEVC后处理算法。该算法首先构造在最大后验概率框架下的优化问题,然后利用解码后的压缩视频和量化参数QP获取非局部低秩和自适应量化约束先验信息,最后利用split-Bregman迭代算法来解决所提的优化问题,从而有效去除压缩效应,提升重建视频质量。其中,非局部低秩先验通过构建基于相似块聚类的非局部低秩模型来获得;自适应量化约束先验通过联合不同量化参数QP下的约束特性与视频的DCT域块活动性来获得。实验结果表明,在同等码率的情况下,与HEVC标准相比,所提算法在帧内编码模式下可以达到平均0.2597 dB的PSNR提升,在帧间编码模式下可以达到平均0.2828 dB的PSNR提升。     

基于残差分布特性的运动补偿算法

视频编码中离散余弦变换(DCT)对自然图像有很好的去相关性，而对运动残差的去相关性较差。针对DCT对残差编码效率较低的缺陷，该文提出一种基于残差分布特性的运动补偿算法，即将残差中所有非零量化DCT系数分为I/P系数，针对不同类型的DCT系数进行分类(帧内或帧间)运动补偿。在MPEG4的编码质量对比实验中，推荐算法比标准算法提高了2 dB以上，从而表明推荐算法能有效地提高运动残差的编码效率。     

基于数学形态学的视频帧间压缩算法研究与实现

常用视频压缩算法DCT存在方块效应和飞蚊噪声,在低速率传输下更为明显,难以满足视频传输中带宽窄、质量高的要求。基于数学形态学的视频图像帧间编码技术,是运用帧间运动补偿的编码算法,该算法通过复合形态变换,用结构元素对宏块中的不规则小块进行开、闭运算,选取并滤出不规则小块,再对不规则小块的大小和运动矢量进行运动估计编码,采用绝对差分和找出匹配块,实现帧间预测。通过仿真可达到比较精细的补偿,提高了编码效率,在相同压缩比下可提高视频质量,弥补了DCT算法在图像传输中的不足,改善了图像传输效果,降低了运算复杂度,对网络视频图像的编解码及传输具有一定的实用意义。     

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。     

基于小波分析的低比特率图像压缩编码方法

本文提出一种基于小波分析的低比特率图像压缩编码方法.首先对图像进行小波分解,然后对高频子图像进行分类矢量量化(CVQ),对低频子图像采用DPCM加熵编码方法.利用不同分辨率级之间子图像的相似性,将最低分辨率级的子图像矢量编码信息用于高层分辨率级子图像矢量码书的训练,提高了码书生成速度;将最低分辨率级子图像的矢量编码信息作为整个图像的编码数据,提高了压缩比和编码速度,和现有文献给出的结果进行比较,该算法具有良好的性能.     

码书自适应的大规模体数据矢量量化算法

针对大规模体数据矢量量化VQ(Vector Quantisation)编码时间长的问题,提出一种基于图形处理器的码书自适应的大规模体数据矢量量化算法。该算法首先提取原始体数据密度分布特征,据此选取合适的初始码书生成算法,将矢量数据分批先后载入图形处理器进行并行计算,每读入一批数据,根据该批数据的码准值对第一批数据产生的码书进行优化及扩充,随后完成该批数据的编码。实验结果表明,该算法提高了图像的编码速度及还原质量,明显缩短了图像的压缩时间,同时保证了体数据重构质量。     

自适应二阶码书矢量量化算法研究

介绍了矢量量化的基本算法LBG。在此基础上,提出了一种时间复杂性和空间复杂性有所降低的改进算法,节省了存储空间,提高了压缩比。该算法的核心是自适应的生成初始码书以及对初始码书的二次压缩过程。从降低时间复杂度和节省存储空间、提高压缩比的角度,对该算法进行了讨论。     

基于矢量量化的数字视频压缩技术

对基于矢量量化的数字视频压缩技术进行了论述，并根据分析结果提出了一种新的矢量量化算法——多级矢量量化联合编码算法，该算法保持了多级矢量量化算法编码速度快、图像相关性小等特点，同时提高了编码质量。实验表明，该算法与已有的算法相比具有较高的图像质量和更高的压缩率。     

Vector-quantization-based video codec for software-only playback on personal computers

This paper discusses a video compression and decompression method based on vector quantization (VQ) for use on general purpose computer systems without specialized hardware. After describing basic VQ coding, we survey common VQ variations and discuss their impediments in light of the target application. We discuss how the proposed video codec was designed to reduce computational complexity in every principal task of the video codec process. We propose a classified VQ scheme that satisfies the data rate, image quality, decoding speed, and encoding speed objectives for software-only video playback. The functional components of the proposed VQ method are covered in detail. The method employs a pseudo-YUV color space and criteria to detect temporal redundancy and low spatial frequency regions. A treestructured-codebook generation algorithm is proposed to reduce encoding execution time while preserving image quality. Two separate vector codebooks, each generated with the treestructured search, are employed for detail and low spatial frequency blocks. Codebook updating and sharing are proposed to further improve encoder speed and compression.     

几种典型视频加密算法的性能评价

视频加密对于保护视频数据的安全具有重要作用。将已有的几种典型MPEG视频加密算法，根据加密过程与压缩编码过程的关系不同，分为4类：完全加密算法，部分加密算法，DCT系数加密算法和熵编码过程加密算法，并从算法的安全性、压缩比、计算复杂度和可操作性几个方面对算法的性能分别做了评价，给出了理论分析和实验结果；结合各类算法的特点，指出了它们各自适应的应用场合。     

Weighted centroid neural network for edge preserving imagecompression

An edge preserving image compression algorithm based on an unsupervised competitive neural network is proposed. The proposed neural network, the called weighted centroid neural network (WCNN), utilizes the characteristics of image blocks from edge areas. The mean/residual vector quantization (M/RVQ) scheme is utilized in this proposed approach as the framework of the proposed algorithm. The edge strength of image block data is utilized as a tool to allocate the proper code vectors in the proposed WCNN. The WCNN successfully allocates more code vectors to the image block data from edge area while it allocates less code vectors to the image black data from shade or non-edge area when compared to conventional neural networks based on VQ algorithm. As a result, a simple application of WCNN to an image compression problem gives improved edge characteristics in reconstructed images over conventional neural network based on VQ algorithms such as self-organizing map (SOM) and adaptive SOM.     

Vector quantization in DCT domain using fuzzy possibilistic c-means based on penalized and compensated constraints

In this paper, fuzzy possibilistic c-means (FPCM) approach based on penalized and compensated constraints are proposed to vector quantization (VQ) in discrete cosine transform (DCT) for image compression. These approaches are named penalized fuzzy possibilistic c-means (PFPCM) and compensated fuzzy possibilistic c-means (CFPCM). The main purpose is to modify the FPCM strategy with penalized or compensated constraints so that the cluster centroids can be updated with penalized or compensated terms iteratively in order to find near-global solution in optimal problem. The information transformed by DCT was separated into DC and AC coefficients. Then, the AC coefficients are trained by using the proposed methods to generate better codebook based on VQ. The compression performances using the proposed approaches are compared with FPCM and conventional VQ method. From the experimental results, the promising performances can be obtained using the proposed approaches.     

Combining support vector machine learning with the discrete cosine transform in image compression

We present an algorithm for the application of support vector machine (SVM) learning to image compression. The algorithm combines SVMs with the discrete cosine transform (DCT). Unlike a classic radial basis function networks or multilayer perceptrons that require the topology of the network to be defined before training, an SVM selects the minimum number of training points, called support vectors, that ensure modeling of the data within the given level of accuracy (a.k.a. insensitivity zone /spl epsi/). It is this property that is exploited as the basis for an image compression algorithm. Here, the SVMs learning algorithm performs the compression in a spectral domain of DCT coefficients, i.e., the SVM approximates the DCT coefficients. The parameters of the SVM are stored in order to recover the image. Results demonstrate that even though there is an extra lossy step compared with the baseline JPEG algorithm, the new algorithm dramatically increases compression for a given image quality; conversely it increases image quality for a given compression ratio. The approach presented can be readily applied for other modeling schemes that are in a form of a sum of weighted basis functions.     

基于DCT频域条件帧间补偿法的视频编码方案及其实现

运动图象的压缩是实现多媒体通信的关键技术之一。本文提出一种基于离散余弦变换系数的条件帧间补偿法及其视频编码方案。该算法利用DCT变换特点和人眼视觉特性，快速高效地对运动图象进行编码。     

基于 DCT 频域条件帧间补偿法的视频编码方案及其实现

运动图象的压缩是实现多媒体通信的关键技术之一。本文提出一种基于离散余弦变换（ＤＣＴ）系数的条件帧间补偿法及其视频编码方案。该算法利用ＤＣＴ变换的特点和人眼视觉特性，快速高效地对运动图象进行编码。由于运算量低，该算法可以在ＰＣ机上用软件实时实现，适用于基于ＰＣ平台的可视电话、会议电视和可视多媒体通信系统。     

基于JPEG的固定背景视频压缩算法研究

为了对固定背景视频进行压缩并获得较高的压缩比,在JPEG静止图像压缩标准的基础上提出了一种新的应用于固定背景视频压缩的算法.对第一帧图像进行JPEG格式的压缩并保存量化后的离散余弦变换系数,对第一帧后的每一帧图像,在进行离散余弦变换和量化后,先同存储器内的第一帧图像的离散余弦变换系数进行异或运算再进行熵编码.通过使用该算法和H.264视频压缩标准对同一段固定背景视频进行压缩并比较压缩后的数据量,表明了该算法具有较高的压缩比.     

A Fast Algorithm for Reconstructing Motion-Compensated Blocks in Compressed Domain

Many advanced video applications require processing compressed video signals. For compression systems using the discrete cosine transform (DCT) with motion compensation, we propose an algorithm with adaptive low-pass filtering (ALPF) to reconstruct blocks with motion vectors in the compressed domain. Compared with the previous work, this algorithm is faster and reduces blocky artifacts caused by quantization.