矢量量化高光谱数据压缩性能评估

性能评估是高光谱数据有损压缩研究的一个关键问题。本文在分析三种典型的基于矢量量化压缩方案的基础上,以K-means聚类准确率的仿真统计比较了三种方案的性能优劣;提出一种失真标准抽取的性能评估框架,在缺乏背景资料的情况下,该框架可以对压缩方案性能给出直观评价,方便了压缩方案的选择及应用。     

Multiscale Representation and Compression of 3-D Point Data

A compact representation scheme is presented for 3-D point data. To describe underlying surface from raw point samples, we dyadically divide a 3-D domain enclosing whole points. Then, local points in each cube are approximated by a plane patch, yielding a multiscale representation of 3-D surface. To reduce the redundancy between different scale models, the geometry innovation is evaluated between different scale planes, which reveals the Euclidian distance between planes. Finally, the geometry innovation coefficients are compressed by a zerotree-based encoder. Based on the multiscale plane representation of 3-D geometry and the efficient plane decomposition method, the proposed scheme provides a desirable framework for 3-D point geometry processing.     

Length Estimation in 3-D Using Cube Quantization

Estimators for the original length of a continuous 3-D curve given its digital representation are developed. The 2-D case has been extensively studied. The few estimators that have been suggested for 3-D curves suffer from serious drawbacks, partly due to incomplete understanding of the characteristics of digital representation schemes for 3-D curves.The selection and thorough understanding of the digital curve representation scheme is crucial to the design of 3-D length estimators. A comprehensive study on the digitization of 3-D curves was recently carried out. It was shown that grid intersect quantization and other 3-D curve discretization schemes that lead to 26-directional chain codes do not satisfy several fundamental requirements, and that cube quantization, that leads to 6-directional chain codes, should be preferred.The few 3-D length estimators that have been suggested are based on 26-directional chain coding that naturally provides a classification of the chain links, which is necessary for accurate length estimation. Cube quantization is mathematically well-behaved but the symmetry and uniformity of the 6-directional digital chain elements create a challenge in their classification for length estimation.In this paper length estimators for 3-D curves digitized using cube quantization are developed. Simple but powerful link classification criteria for 6-directional digital curves are presented. They are used to obtain unbiased length estimators, with RMS errors as low as 0.57% for randomly oriented straight lines.     

BAQ算法在SAR原始数据压缩中的应用研究

研究了用块自适应量化（BAQ）算法压缩合成孔径雷达（SAR）原始数据,考虑了实际SAR数据的非零均值。文中给出了算法在信号域内的信号对量化噪声比（RSQN）和均方根相位误差这两个性能指标的评估结果;并给出了SAR原始数据经BAQ处理后的成像结果;最后提出了一种可与雷达接收机增益控制电路相结合的、压缩比灵活可变的BAQ方案,适合于在压缩比和图像质量之间作折衷选择。     

非线性空间几何收缩的分形图象压缩编码

在经典的空间几何线性均值收缩算法的基础上，提出了一种非线性空间几何收缩算法。由实验表明，该算法不仅能提高压缩比，而且对信噪比也有一定的改善。     

基于曲率的三维规则表面网格数据压缩方法

采用一些传统算法压缩三维图形数据时,发现压缩比虽高,但是在压缩过程中很容易忽略那些相对于整体变化细微而局部变化激烈的子区域,而且对于曲线的微小变化很敏感,以致于重建效果不理想。本文以曲率作为判据,克服了上述缺点,能对于上述区域尽量加以保留。实验数据表明,压缩后的重建效果较为理想。     

矢量量化在图像压缩编码中的应用进展

矢量量化充分利用图像的相关性,为实现图像的高倍压缩提供了新途径。本文就目前的研究进展,从矢量量化器的形成、编码算法及在图像压缩编码中的应用进行了综合性的描述。     

基于插值矢量量化的地形金字塔纹理压缩

矢量量化因其压缩比大,解码简单效率高成为实时绘制系统中大规模纹理压缩主要使用的方法。文献[1]介绍的基于矢量量化方法针对纹理金字塔压缩取得了很大的成功。在此基础上使用一个特征值数据本,一个残差矢量码本和索引表对金字塔纹理的各层进行压缩,在保持实时性的同时提高了压缩比。使用了改进的SOFM码本生成算法,实验表明其码本质量优于传统的码本生成算法。     

一种压缩比自适应的快速矢量量化算法

提出了一种压缩比自适应的矢量量化(VQ)编解码算法,将图像预先分为16×16的分块,根据图像块的平滑程度,减少重复搜索的运算量,大幅提高压缩比和编码速度,而解码图像的峰值信噪比(PSNR)只有很少下降。对于10幅标准图像的测试结果表明,与普通VQ相比,该文算法的压缩比平均提高54%,PSNR平均仅降低0.86%。对于单纯背景的图像,压缩比可达到200∶1左右。算法简单,适合硬件实现。     

Reducing and Filtering Point Clouds With Enhanced Vector Quantization

Modern scanners are able to deliver huge quantities of three-dimensional (3-D) data points sampled on an object's surface, in a short time. These data have to be filtered and their cardinality reduced to come up with a mesh manageable at interactive rates. We introduce here a novel procedure to accomplish these two tasks, which is based on an optimized version of soft vector quantization (VQ). The resulting technique has been termed enhanced vector quantization (EVQ) since it introduces several improvements with respect to the classical soft VQ approaches. These are based on computationally expensive iterative optimization; local computation is introduced here, by means of an adequate partitioning of the data space called hyperbox (HB), to reduce the computational time so as to be linear in the number of data points N, saving more than 80% of time in real applications. Moreover, the algorithm can be fully parallelized, thus leading to an implementation that is sublinear in N. The voxel side and the other parameters are automatically determined from data distribution on the basis of the Zador's criterion. This makes the algorithm completely automatic. Because the only parameter to be specified is the compression rate, the procedure is suitable even for nontrained users. Results obtained in reconstructing faces of both humans and puppets as well as artifacts from point clouds publicly available on the web are reported and discussed, in comparison with other methods available in the literature. EVQ has been conceived as a general procedure, suited for VQ applications with large data sets whose data space has relatively low dimensionality     

基于小波变换和矢量量化的人脸图象压缩

在图象的压缩编码中，矢量量化可以利用某特定类图象（加人脸）的统计特性，为了在高压缩比下获得较好的压缩效果，提出了一种新的小波变换域内进行矢量量化的算法，该算法用树结构表示小波变换域系数，并根据各节点值的重要程度，从每一棵树中提取一个矢量，然后进行矢量量化；解码时，为了使矢量分量能正确地返回到原来树中的正确位置，需利用EZW^[1]、SPIHT^[2]算法的思想标记这棵树，因为这样才能充分利用父子相关性和兄弟相关性，从而显著地减少了标记信息，在提取矢量时，可用简单的阈值剪枝算法，也可用SFQ^[3]的最佳剪枝算法，而且后者能进一步提高峰值信噪比，用该算法对人脸图象进行的压缩试验结果表明，在高压缩比（100:1左右）下，恢复的图象质量（视觉效果和峰值信噪比）比通常的小波压缩算法（如EZW，SPIHT、SFQ等）好得多，该算法特别适合于对特定类图象的压缩。     

基于矢量量化的快速图像检索

传统索引方法对高维数据存在"维数灾难"的困难.而对数据分布的精确描述及对数据空间的有效划分是高维索引机制中的关键问题.提出一种基于矢量量化的索引方法.该方法使用高斯混合模型描述数据的整体分布,并训练优化的矢量量化器划分数据空间.高斯混合模型能更好地描述真实图像库的数据分布;而矢量量化的划分方法可以充分利用维之间的统计相关性,能够对数据向量构造出更加精确的近似表示,从而提高索引结构的过滤效率并减少需要访问的数据向量.在大容量真实图像库上的实验表明,该方法显著减少了支配检索时间的I/O开销,提高了索引性能.     

基于矢量量化的图象分形压缩编码

提出一种基于矢量量化技术的图象分形压缩编码方法,其能较好地克服传统的图象分形压缩编码方法中存在的速度慢,压缩率低等缺点,并且同时获得较高的图象恢复质量。     

Bidirectional Texture Function Compression Based on Multi-Level Vector Quantization

The Bidirectional Texture Function (BTF) is becoming widely used for accurate representation of real-world material appearance. In this paper a novel BTF compression model is proposed. The model resamples input BTF data into a parametrization, allowing decomposition of individual view and illumination dependent texels into a set of multi-dimensional conditional probability density functions. These functions are compressed in turn using a novel multi-level vector quantization algorithm. The result of this algorithm is a set of index and scale code-books for individual dimensions. BTF reconstruction from the model is then based on fast chained indexing into the nested stored code-books. In the proposed model, luminance and chromaticity are treated separately to achieve further compression. The proposed model achieves low distortion and compression ratios 1:233–1:2040, depending on BTF sample variability. These results compare well with several other BTF compression methods with predefined compression ratios, usually smaller than 1:200. We carried out a psychophysical experiment comparing our method with LPCA method. BTF synthesis from the model was implemented on a standard GPU, yielded interactive framerates. The proposed method allows the fast importance sampling required by eye-path tracing algorithms in image synthesis.     

Variable-resolution Compression of Vector Data

The compression of spatial data is a promising solution to reduce the space of data storage and to decrease the transmission time of spatial data over the Internet. This paper proposes a new method for variable-resolution compression of vector data. Three key steps are encompassed in the proposed method, namely, the simplification of vector data via the elimination of vertices, the compression of removed vertices, and the decoding of the compressed vector data. The proposed compression method was implemented and applied to compress vector data to investigate its performance in terms of the compression ratio, distortions of geometric shapes. The results show that the proposed method provides a feasible and efficient solution for the compression of vector data, is able to achieve good compression ratios and maintains the main shape characteristics of the spatial objects within the compressed vector data.

向量相似度可复原三维点云压缩算法

针对三维点云数据压缩中细节特征不易保留,模型平缓部位存在过度压缩以及压缩后的点云模型不易复原等问题,提出一种基于向量相似度的三维点云压缩算法和复原算法CVS。向量相似性度量采用提出的L3A进行度量。CVS把每个三维坐标点看作是连接其坐标和原点的三维向量,按照三维坐标点的读入顺序选取参考向量,生成覆盖整个点云区域的采样区域,进行分区压缩。在采样区域中使用最小二乘曲面拟合算法对包含其中的点云进行曲面拟合,设置曲率阈值剔除坐标点,并存储曲面方程参数用于复原。通过控制L3A向量相似度中的长度和角度的变化阈值,使得密集点云区域的压缩率高于非密集区域的压缩率,通过控制曲率阈值,使得低曲率区域的压缩率高于高曲率区域的压缩率,最大程度保留模型细节特征。CVS使用压缩阶段产生的复原信息生成点云来恢复模型的细节特征,使得模型特征更加明显。     

基于改进进化规划的图像压缩中的矢量量化

提出了基于改进进化规划和LBG算法的一种新的矢量量化算法。由于进化规划中十进制编码方案的收敛速度比二进制编码方案快,因此在进化规划中采用十进制编码方案。同时,根据图像压缩的特点,为了提高算法性能,该文采用一种自适应的多位变异操作。之后将改进的进化规划引入到图像压缩的矢量量化问题中。实验表明,新算法在提高恢复图像质量方面比传统方法优越。     

一种格矢量量化半易损水印算法

在抵抗JPEG压缩的同时保持较好的篡改检测能力是用于图像内容认证的半易损水印的关键问题.提出一种基于格的半易损水印算法,将图像的DCT变换系数分组量化为高维空间中的格点,通过比较格点坐标与DCT系数之间的关系自适应地在格点中嵌入水印信息.实验结果表明,与同类算法相比该算法具有较好的性能,含水印图像的峰值信噪比超过49dB,在抵抗质量因子为50的JPEG压缩的情况下仍具有较好的篡改检测和定位能力.     

Recursive 3-D Visual Motion Estimation Using Subspace Constraints

A structure from motion algorithm is described which recovers structure and camera position, modulo a projective ambiguity. Camera calibration is not required, and camera parameters such as focal length can be altered freely during motion. The structure is updated sequentially over an image sequence, in contrast to schemes which employ a batch process. A specialisation of the algorithm to recover structure and camera position modulo an affine transformation is described, together with a method to periodically update the affine coordinate frame to prevent drift over time. We describe the constraint used to obtain this specialisation.Structure is recovered from image corners detected and matched automatically and reliably in real image sequences. Results are shown for reference objects and indoor environments, and accuracy of recovered structure is fully evaluated and compared for a number of reconstruction schemes. A specific application of the work is demonstrated—affine structure is used to compute free space maps enabling navigation through unstructured environments and avoidance of obstacles. The path planning involves only affine constructions.     

粒子群优化人体星形骨架特征矢量量化

实现了人体星形骨架特征提取,并利用粒子群优化骨架特征矢量的量化。星形骨架通过连接人体质心点到人体四肢及头部端点实现,是一种快速骨架提取技术。把质心点和端点连接,人体星型骨架可以用一个五维矢量Si表示,Si∈Rn,Rn是星型骨架特征空间。时序图像中的人体动作可以用星型骨架序列表示的特征矢量序列S代替。最后用粒子群优化S的特征量化过程,生成特征码本G。