基于DCT的SAR原始数据压缩算法分析

根据合成孔径雷达(SAR)原始数据经离散余弦变换(DCT)后的系数特性,分析了在DCT域按各子带能量分布作变比特块自适应量化的算法--DCT域块自适应量化(DCT-BAQ),并将该方法与时域BAQ算法作了比较.结合一组实测SAR原始数据,用两种算法分别进行了压缩和解压缩,并计算了数据域及图像域信噪比,给出了两种压缩算法所成的图像.实验证明,在相同的比特率下,DCT-BAQ算法的数据域和图像域信噪比均比时域BAQ算法高.数据域信噪比增加1.38～1.94 dB,图像域信噪比增加1.56～1.91 dB.加之DCT变换是实数运算、有快速算法,所以将它用于SAR原始数据压缩有一定的优越性.     

基于DCT的分块自适应量化算法及其用于SAR原始数据压缩

该文提出了一种基于离散余弦变换(DCT)和分块自适应量化相结合的SAR原始数据压缩算法。利用SAR原始数据满足局部平稳高斯随机过程的特点,通过将DCT系数进行重排,并对重排后的系数矩阵进行有效的量化比特分配和分块自适应量化,从而大幅度提高了量化增益。通过对真实SAR原始数据的压缩实验结果表明:该文算法与BAQ算法相比,以相对较低的运算复杂度增加,使图像域的压缩性能指标有了明显提高。     

SAR原始数据时频域幅相压缩算法

采用时频分析的方法,结合SAR原始数据非平稳的时频振荡特性,提出了一种基于Gabor变换的SAR原始数据时频域幅相压缩算法.该算法首先对分块原始数据进行幅度-相位转换、2D-RDGT变换以及在时频域内对各频率平面根据方差进行比特分配,然后采用分块自适应量化算法量化各频率平面.实验结果表明,该算法与传统的BAQ算法和已有的幅相算法相比无论在数据域还是在图像域的压缩性能都取得了明显的改善.     

网格编码矢量量化在SAR原始数据压缩中的应用

本文提出了一种结合传统的块自适应量化与网格编码矢量量化对SAR原始数据进行压缩的方法。首先对SAR原始数据实施块自适应量化以减小数据的动态范围,然后对块自适应量化后的数据进行网格编码矢量量化。该方法在考虑了SAR原始数据特性的基础上较好地利用了矢量的空间相关性和信号的时间相关性,进一步提高了量化增益。仿真结果表明,该方法在SAR原始数据的压缩中取得了很好的效果。     

一种距离聚焦后的SAR原始数据压缩算法

由于合成孔径雷达(SAR)原始数据的相关性很低,直接压缩原始数据是比较困难的。该文提出一种新算法,先对SAR原始数据作距离聚焦处理,使其在方位向具有较强的相关性,再沿方位向作矢量线性预测,并对预测残差序列作分块自适应量化。结合一组实测SAR原始数据,用3种算法分别进行了压缩和解压缩,并计算了数据域及图像域信噪比,给出了3种压缩算法所成的图像。实验表明,在相同比特率条件下,该文算法得到的数据域信噪比和图像域信噪比均比BAQ算法高。该文算法的计算量远小于有关文献给出的距离聚焦后的压缩方法。     

基于DCT-TCQ的SAR原始数据压缩算法

该文提出了一种基于离散余弦变换(DCT)和网格编码量化(TCQ)的SAR原始数据压缩算法。SAR原始数据可以看成是距离向和方位向的2维线性调频信号的线性平移叠加,因而含有丰富的频率分量,对DCT系数按频率分组,根据各组频率分量系数方差确定量化比特分配,然后对量化系数进行TCQ编码量化,可以大幅度提高了量化增益。真实SAR原始数据实验结果表明:该算法使SAR原始数据的压缩性能指标较现有算法有了明显提高。     

结合相干斑抑制的小波域SAR图像网格编码量化

本文结合SAR图像相干斑的抑制,研究了一种在小波域对带噪SAR图像做网格编码量化的新方法。首先将SAR图像在小波域内实施软阀值去噪声,然后根据SAR图像在小波域中各子带系数固有的树结构关系对其进行零树分类,对分类后的重要性小波系数进行网格编码量化,利用卷积编码和信号空间扩展来增大量化信号间的欧氏距离,并用维特比算法寻找最优量化序列。该方法综合了小波相干斑抑制、零树编码、网格编码量化技术,不仅利用了信号小波变换域的空间相关性,而且也较好地利用了信号间的时间相关性。在压缩的同时进行了相干斑抑制,在SAR图像的压缩中取得了很好的效果。     

基于2D实值离散Gabor变换的SAR原始数据压缩

该文采用时频分析的方法,结合合成孔径雷达（SAR）原始数据的线性调频特性,提出了一种基于2D实值离散Gabor变换的SAR原始数据压缩方法。该方法对I,Q两路原始数据进行分块,先对每一块进行二维实值离散Gabor变换（2D—RDGT）,在时频平面内对各频率平面根据方差进行比特分配,然后采用BAQ量化各频率平面。用同一块数据进行压缩解压缩成像实验并与已有的方法进行比较,实验结果表明该文提出的方法在数据域和图像域的性能都优于已有的方法。     

基于小波的SAR原始数据幅相压缩算法

根据SAR原始数据幅值和相位统计独立的分布特点,该文在分析了SAR原始数据幅值的小波域系数特性后,研究了对SAR原始数据的幅值和相位分别进行量化的基于小波变换的幅相压缩算法。文中采用此算法对实际合成孔径雷达数据进行了压缩,结果表明,与传统的BAQ算法相比,该算法在很好保持数据的相位信息的同时,压缩效果有了明显的改善。     

基于Brushlet变换的SAR图像SPIHT压缩算法

提出一种基于Brushlet多方向变换特点的SPHIT编码算法,并将其应用于SAR图像压缩编码中,通过对真实SAR图像的压缩实验表明,相比于传统的小波编码方法,Brushlet变换域的SPHIT算法能获得更好的压缩性能,提高了解码后的PSNR值,并且较好地保留了图像细节特征和提高了可视性.     

合成孔径雷达原始数据压缩

本文首先论述了合成孔径雷达原始数据压缩的意义和重要性,接着简要回顾了它的发展历史,着重讨论了分块自适应量化(BAQ)和矢量量化(VQ)两种数据压缩方法.最后展望了合成孔径雷达原始数据压缩的发展前景.     

基于高斯分布判决的SAR原始数据压缩方法

针对合成孔径雷达(SAR)数据概率密度函数偏离高斯分布时分块自适应量化(BAQ)压缩算法性能下降的问题,提出一种判断SAR数据是否符合高斯分布的新方法,并将其应用于SAR原始数据压缩。该方法借助几何关系将高斯曲线用等面积的三角形替代,计算出二者的交点坐标,并对各交点范围内二者的积分值进行比较,得到三个判决条件;然后,将满足这些条件的SAR数据判断为标准高斯分布数据并进行BAQ压缩,对判断为非高斯分布的数据采用矢量量化方法压缩。最后,实测数据的仿真实验说明本文方法的性能优于BAQ和文献[9]的方法。     

基于实测数据的原始数据压缩对InSAR系统测高影响研究

为了研究原始数据压缩对InSAR系统干涉测高的影响。该文应用4组典型高程起伏的ERS-1/2实测数据,对Block Adaptive Quantization (BAQ) 与 Amplitude-Phase (AP) 两种有重要工程应用潜力的算法对干涉测高影响进行研究。试验结果表明:BAQ算法在设计时,要合理选择数据分块尺寸;同时,对于测高精度要求较高且垂直基线较短的系统,量化位数应至少为4 bit。AP算法在设计时,要合理分配幅度和相位量化位数。对于平均比特率为3 bit/sample,AP算法最优方案为幅度2 bit、相位4 bit,其性能介于3 bit BAQ和4 bit BAQ之间。     

Adaptive block transform coding of speech based on LPC vectorquantization

The authors describe several adaptive block transform speech coding systems based on vector quantization of linear predictive coding (LPC) parameters. Specifically, the authors vector quantize the LPC parameters (LPCVQ) associated with each speech block and transmit the index of the code vector as overhead information. This code vector will determine the short-term spectrum of the block and, in turn, can be used for optimal bit allocation among the transform coefficients. In order to get a better estimate of the speech spectrum, the authors also consider the possibility of incorporating pitch information in the coder. In addition, entropy-coded zero-memory quantization of the transform coefficients is considered as an alternative to Lloyd-Max quantization. An adaptive BTC scheme based on LPCVQ and using entropy-coded quantizers is developed. Extensive simulations are used to evaluate the performance of this scheme     

A novel algorithm and architecture of combined direct 2-D transform and quantization for H.264

This paper proposes a novel high-performance direct 2-D transform algorithm which suitably arranges the data processing sequences adopted in row and column transforms of H.264 CODEC systems to finish the data transposition. Simultaneity, this paper proposes a new direct 2-D transform and quantization architectures for H.264 video coding standard. The induced new transform and quantization architecture greatly increases the data processing rate and eliminates transform multiplication and transpose memory, and select different mode and quantization according to AC coefficient, DC coefficient, chrominance block and Luminance block. And this architecture just need to storage one quantization tables for Integer transform and Hadamard transform, but it can do two types of forward transforms and quantization just in one block.     

An Integrated Systolic Array Design for Video Compression

This paper presents an integrated systolic array design for implementing full-search block matching, 2-D discrete wavelet transform, and full-search vector quantization on the same VLSI architecture. These functions are the prime components in video compression and take a great amount of computation. To meet the real-time application requirements, many systolic array architectures are proposed for individually performing one of those functions. However, these functions contain similar computational procedure. The matrix-vector product forms of the three functions are quite analogous. After extracting the common computation component, we design an integrated one-dimensional systolic array that can perform aforementioned three functions. The proposed architecture can efficiently perform three typical functions: (1) the full-search block matching with block of size 16 × 16 and the search are from –8 to 7; (2) the 2-D 2 level Harr transform with block of size 8 × 8; and (3) the full-search vector quantization with input vector of size 2 × 2. A utilization rate of 100% to 97% is achieved in the course of executing full-search block matching and full-search vector quantization. When it comes to perform 2-D discrete wavelet transform, the utilization rate is about 32%. The proposed integrated architecture has lowered hardware cost and reduced hardware structure. It befits the VLSI implementation for video/image compression applications.     

Performance of entropy-constrained block transform quantizers

An analysis of the rate-distortion performance of an entropy-constrained block transform quantization scheme operating on first-order stationary Gauss-Markov sources is presented. Uniform threshold quantization is employed to quantize the transform coefficients. An algorithm for optimum stepsize (or, equivalently, entropy) assignment among the quantizers is developed and a simple asymptotic formula indicating the high-rate performance of the block transform quantization scheme is presented. Specific results determining the rate-distortion performance of the entropy-constrained block transform quantization scheme operating upon first-order Gauss-Markov sources are presented, and comparisons are made with the Huang and Schultheiss (1963) block transform quantization, vector quantization, and predictive quantization     

一种新的二维自适应提升小波变换方法

基于图像的局部特性和统计信息,该文提出一种新的二维自适应提升小波变换方法。该方法通过构造基于图像局部特性的自适应更新算子和基于图像统计信息的非线性预测算子,对图像进行自适应的提升小波变换,与文献中的自适应小波变换方法和非自适应小波变换方法进行对比实验,该文方法所得到的高频子带的熵更低,含零高频系数更多,更有利于图像的压缩编码。