基于FPGA的二维DCT变换的实现

二维离散余弦变换(DCT)在图像处理和视频编码中起重要的作用。以MPEG 2全I帧编码为背景,在现场可编程门阵列(FPGA)上实现8×8像素的二维DCT变换。算法首先把8×8像素的二维DCT变换化简成8次一维DCT变换加上适当的蝶形运算和顺序重排操作。试验表明,方案可以只用一个一维DCT模块实现输入采样率为74.25MHz的二维DCT变换。     

离散余弦变换的改进的算术傅立叶变换算法

离散余弦变换(DCT)是数字图像处理等许多领域的重要数学工具.本文通过一种新的傅立叶分析技术——算术傅立叶变换(AFT)来计算DCT.本文对偶函数的AFT进行了改进.改进的AFT算法不但把AFT所需样本点数减少了一半,从而使所需加法计算量减少了一半,更重要的是它建立起AFT和DCT的直接联系,因而提供了适合用于计算DCT的AFT算法.本文推导了用改进的AFT计算DCT的算法并对算法进行了简要的分析.这种算法的乘法量仅为O(N),并且具有公式一致,结构简单,易于并行,适合VLSI设计等特点,为DCT的快速计算开辟了新的途径.     

基于DCT的实值离散Gabor变换

该文提出了一种基于离散余弦变换(DCT)的实值离散Gabor变换(RDGT),不仅适用于临界抽样条件而且适用于过抽样条件,并证明了变换的完备性条件。由于这种变换仅涉及实值计算,并且可利用快速DCT,IDCT算法来加速运算,因此比传统复值离散Gabor变换在计算和实现方面更为简单,必将有效地提高非平稳信号与图像的分析、处理速度和效率。     

基于ADSP—BF533的H．264DCT变换及量化的优化实现

根据ITU-T提出的H.264视频编解码标准,对JM算法及TI Blackfin 533 DSP自身特点进行了分析,将标准中编码采用的整数离散余弦变换(DCT)、量化,解码采用的反DCT变换、反量化的JM算法成功移植到DSP上面,同时根据DSP的特点进行软硬件优化,达到了较好效果。     

一种基于Arnold置乱和离散余弦变换的信息隐藏算法

介绍了基于Arnold置乱变换和离散余弦变换(DCT)的图像信息隐藏算法.首先对秘密图像进行Arnold置乱变换,形成秘密信息,然后对载体图像进行DCT变换,秘密信息通过量化处理嵌入到DCT域中.从试验结果来看,该算法在一定程度上满足了信息隐藏的鲁棒性要求.     

长度为P^l的离散余弦变换算法

本文对长度为p′(P为任意自然数)的余弦变换DCT-Ⅱ提出了一种快速算法,并对如何减少运算量做了详细讨论。当P=2时,本算法和目前最快算法的运算量一样,且结构简单。结合B.G.Lee提出的把一维DCT转化为多维DCT计算的方法可计算任意长度的DCT-Ⅱ。     

任意长离散余弦变换的快速递归算法

该文基于Clenshaw递归公式以及离散余弦自身的对称性提出任意长离散余弦变换(DCT)的一种并行递归快速算法,给出了该算法的滤波器实现结构;与现有的其它递归算法以及基于算术傅里叶变换的余弦变换算法进行了计算复杂度的比较分析,结果表明该文算法运算量大大减少。该递归计算的滤波器结构使算法非常适合大规模集成电路(VLSI)实现。     

二维离散余弦变换模块算法及其Hierarchical物理实现

本文阐述了离散余弦变换(DCT)的算法结构及其优化,并基于cadence的EDA平台,诠释了自上而下(Top-Down)的Hierarchical分层次芯片设计理念及方法.     

二维离散余弦变换/逆离散余弦变换电路及方法

为了实现二维离散余弦变换(DCT)/逆离散余弦变换(IDCT),本文提供一种二维离散余弦变换/逆离散余弦变换电路,采用一个加法器和两个移位器代替一个乘法器,通过选择特定的系数,使得硬件电路无需使用耗费资源较多、速度较慢的乘法器,是一种高效的无乘法器的DCT变换电路。该电路只需要很少的加法器和移位器,并可以达到很高的精度。     

基于CUDA的DCT快速变换实现方法

为了加快DCT快速变换的处理速度,提出了一种基于CUDA在图形处理器(GPU)上实现DCT快速变换的方法,其中主要利用DCT变换中各分块之间的独立性适合GPU并行处理架构的特点,把传统串行DCT快速变换算法映射到CUDA并行编程模型,并从线程分配,内存使用,硬件资源划分等方面进行优化,来充分利用GPU的巨大运算能力,实验表明,该方法能有效地提高D阻快速变换的速度.     

Fast DCT domain filtering using the DCT and the DST

A method for efficient spatial domain filtering, directly in the discrete cosine transform (DCT) domain, is developed and proposed. It consists of using the discrete sine transform (DST) and the DCT for transform-domain processing on the in JPEG basis of the previously derived convolution-multiplication properties of discrete trigonometric transforms. The proposed scheme requires neither zero padding of the input data nor kernel symmetry. It is demonstrated that, in typical applications, the proposed algorithm is significantly more efficient than the conventional filtered spatial domain and earlier proposed DCT domain methods. The proposed method is applicable to any DCT-based image compression standard, such as JPEG, MPEG, and H.261.     

Highly-compacted DCT coefficients

In this paper highly-compacted DCT coefficients (HDCT) are presented. This compactness is achieved by sorting in ascending order the data first, then by applying the Discrete Cosine transform (DCT) to the ordered data. Images are highly correlated. DCT exhibits excellent energy compaction. It will be shown that HDCT has much better energy compactness than the DCT. This has the effect of representing every ordered image with very small number of HDCT coefficients (dimensionality reduction). The compression capabilities of the HDCT are presented. HDCT is also applied to face recognition problem. Simulation results on different databases showed high average success rate of this algorithm compared to other algorithms.     

Prime-factor DCT algorithms

In this correspondence, new algorithms are presented for computing the l-D and 2-D discrete cosine transform (DCT) of even length by using the discrete Fourier transform (DFT). A comparison of the proposed algorithms to other fast ones points out their computational efficiency, which is mainly based on the advantages of prime-factor decomposition and a proper choice of index mappings     

Compression of hyperspectral imagery using the 3-D DCT and hybridDPCM/DCT

Two systems are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system uses TCQ to encode transform coefficients resulting from the application of an 8×8×8 discrete cosine transform (DCT). The second systems uses DPCM to spectrally decorrelate the data, while a 2D DCT coding scheme is used for spatial decorrelation. Side information and rate allocation strategies are discussed. Entropy-constrained code-books are designed using a modified version of the generalized Lloyd algorithm. These entropy constrained systems achieve compression ratios of greater than 70:1 with average PSNRs of the coded hyperspectral sequences exceeding 40.0 dB     

Two-stage decomposition of the DCT

The DCT kernel matrix is first decomposed into a block diagonal structure (BDS) with diagonal skew-circular correlated (SCCR) sub-matrices of length 2, 4, ..., N/2 by coset decomposition, and then each of these independent SCCR sub-matrices is further split into two stages by decomposing its elements into a linear combination of other simple basis functions. The preprocessing stage can be treated as a new transform approximated to the DCT, and is suitable for image compression. Various preprocessing stages are obtained by choosing various basis functions. The postprocessing stage is used for converting the preprocessing stage back to the DCT. Both the preprocessing stage and the postprocessing stage are BDSs containing independent diagonal SCCR sub-matrices, thus the fast and parallel computation of both the preprocessing and the postprocessing stages is feasible using methods such as a semisystolic array or distributed arithmetic implementation     

一种应用于8×8二维DCT/IDCT的高效结构

设计实现了二维离散余弦变换和逆变换.采用的DCT快速算法和基于分配算法(DA)的乘法-累加器(MAC)结构,极大地减少了硬件资源需求,并能达到很高的处理速度,计算精度满足CCITT标准要求.     

Matlab下DCT变换在信号压缩中的应用

DCT变换经常被信号处理和图像处理使用,用于对信号和图像进行有损数据压缩.Matlab下DCT变换具有很强的"能量集中"特性,仅使用一部分DCT系数就可以重建信号,并且失真比较小.本文通过两个实例加以说明Matlab下一维及二维DCT变换在信号压缩中的应用.     

基于DCT压缩域的纹理图像分类

该文提出了一种在基于离散余弦变换(DCT,Discrete Cosine Transform)压缩域进行纹理图像分类的方法。此方法主要利用了DCT域能量具有方向性的特点,直接在游程长编码(RLE,Run LengthEncoding)形成的码流中根据这种方向性来提取、组织特征矢量,对纹理图像进行分类。此方法只需对压缩图像进行简单的Huffman解码,要处理的数据量较少,处理速度快。实验结果表明,这种分类方法具有较高的准确性。     

A fast 4×4 DCT algorithm for the recursive 2-D DCT

The authors present an efficient algorithm for the computation of the 4×4 discrete cosine transform (DCT). The algorithm is based on the decomposition of the 4×4 DCT into four 4-point 1-D DCTs. Thus, only 1-D transformations and some additions are required. It is shown that the proposed algorithm requires only 16 multiplications, which is half the number needed for the conventional row-column method. Since the 2^m×2^m DCT can be computed using the 4×4 DCT recursively for any m, the proposed algorithm leads to a fast algorithm for the computation of the 2-D DCT     

三角形网格的分块DCT压缩

在现有的代表性三角形网格压缩方法中,先采用一定的网格遍历方法来压缩连接信息,同时用遍历路径上的相邻顶点来对每个顶点的几何坐标进行预测,以压缩几何信息.其主要缺点是只利用了遍历路径上的相邻顶点来进行预测,并没有充分去掉顶点间的相关性.其实在空间中一定局部范围内,所有顶点的坐标间都存在着一定的相关性,这些顶点虽然在空间上相邻,但并不一定在遍历路径上相邻.和图像压缩标准JPEG的思路类似,本文提出一种新的基于分块DCT的网格几何信息压缩方法.先将网格划分成很多基本同样大小的块,利用每个块内的所有顶点按遍历次序排列成一维序列后,坐标呈周期性分布的事实,采用一维DCT变换来去除块内顶点间的相关性.实验表明,分块DCT方法取得了较好的几何信息压缩性能.