图像压缩中的小波变换

小波变换具有良好的局部特性和空间频率特性,并且具有描述非平稳图像信号的能力和适应人眼视觉特性的良好性能。嵌入式零树小波编码(EZW)算法是小波图像编码研究的一个里程碑。针对EZW不足,提出一种有效的EZW算法改进方案。     

离散余弦小波包变换及语音信号压缩感知

针对语音信号压缩感知问题,在研究语音离散余弦变换(Discrete Cosine Transform,DCT)系数和小波包变换(Wavelet Packet Transform,WPT)特性的基础上构造了离散余弦小波包变换(Discrete Cosine Wavelet Packet Transform,DCWPT)。DCWPT首先获取语音信号的DCT域系数,结合语音频谱特性选取部分DCT系数进行WPT变换,从而得到比DCT系数更加稀疏的DCWPT系数。为将此变换直接用于压缩感知,构造了DCWPT的正交稀疏分解矩阵并分析了其稀疏表示性能。结合稀疏表示基优化了正交匹配追踪重构算法,提出了基于DCWPT的语音信号压缩感知框架。通过压缩重构对照实验,采用主客观评价指标,得出该方法优于传统基于DCT的语音压缩感知方法的结论。     

基于DSP的JPEG图像压缩的设计与实现

介绍一种用TI公司的TMs320C6713高速DSP实现JPEG图像压缩,概述JPEG图像编码算法的基本原理以及在DSP上的实现过程,重点讨论图像编码中DCT变换的实现和优化.     

小波变换用于序列图像编码的研究

系统地研究了小波变换用于序列图像编码的方法，其中包括变换域的位移估值算法、系数差值量化及变字长编码等。采用小波变换进行序列图像编码，恢复图像没有方块效应，主观评价良好。     

MPEG-2编码器中一种新型的DCT/IDCT结构

针对DCT变换编码是MPEG 2标准中运算量较大的部分 ,采用行列分解法设计的 8× 8二维DCT/IDCT结构 ,仅用一个 1维DCT核完成MPEG 2编码的二维DCT/IDCT变换 ,1维DCT核的流水数据宽度为两像素宽 ,较同类结构[1,2 ] 有更好的利用率。该设计是在完成MPEG 2编码器结构实现时提出的 ,整体结构规整 ,适合VLSI实现 ,具有一定的应用价值     

整型DCT变换在医学图像无损压缩中的研究与应用

本论文主要研究并实现了一种基于整型DCT的对图像进行无失真编码的方法。其主要思想为：先在整型FFT基础上构造可逆的DCT整型变换,变换后的数据再用SPIHT算法处理,最后进行算术编码。首先在图像变换和无失真编码等相关图像处理知识的基础上,结合前人的研究经验,对设计进行了理论推导和论证并将其归纳为数学模型,总结出适用算法,并编程实现并证明该方法且具有良好的压缩性能。     

采用离散余弦变换的小波图像去噪方法

提出一种通过对小波域中噪声能量的估计来进行去噪的新方法。算法采用离散余弦变换(DCT)提取小波系数的主要特征,无需对噪声方差进行估计。对图像进行小波分解,利用 DCT对高频子带进行局部特征提取;利用部分 DCT 系数对小波系数进行重建,并以重建系数的平均能量作为局部噪声能量的估计;去除原小波系数中的噪声分量后,进行小波逆变换,得到去噪后的图像。实验证明,其峰值信噪比(PSNR)比通常的阈值萎缩法提高了 2-4dB。     

小波包变换在人脸识别光照补偿中的应用

提出了一种用于非均匀光照条件下人脸识别的光照补偿算法.该算法首先将人脸图像变换到对数域,并在对数域中计算2维小波包变换,通过舍弃部分子带图像中的系数来实现人脸图像的非均匀光照补偿.由于人脸光照补偿的目的是为了提高人脸识别性能,所以光照补偿的效果利用人脸识别率来表征.在Yale B人脸库中,与对数域离散余弦变换(DCT)光照补偿算法进行了比较,实验结果表明,本文方法的人脸识别平均误识率可以达到0.53%,比对数域DCT方法具有更好的性能.     

基于小波变换的图像融合方法研究

图像融合是多传感器信息融合的一个重要分支,小波变换是这一领域研究方法上的重大突破。本文通过对小波变换理论的研究,分析了二维离散小波分解与重构的算法,提出了一种高频小波系数直接替换的算法,并利用MATLAB数学分析工具环境实现全色图像和多光谱图像的融合,实验结果表明这种方法能很好地增强图像的光谱分辨率,便于对图像进行分析和识别。     

采用小波与ALCT的分层图像编码算法

针对纹理较丰富的图像提出了一种分层编码算法。该算法将图像划分成平滑层和纹理层,基于小波变换和自适应局部余弦变换(ALCT)分别编码。为提高平滑层小波零树编码的效率,本算法先对原图像进行恰当的平滑运算,然后再进行小波变换,从而增加零系数的个数。第二层利用改进的折叠运算和快速 DCT 实现残差纹理图像的 ALCT,并且提出了一种 ALCT 系数的重组方案,进而实现了重组系数的嵌入式编码。实验结果表明,本算法在未进行算术编码的情况下,与小波零树 SPIHT 算法相比,峰值信噪比(PSNR)提高了 0.4dB,并在重建图像中更好地保留了原图像的纹理特征。     

Research on adaptive scanning and quantisation for 3D DCT video coding

Abstract

Nowadays, a well-established video coding method is based on the block-matching algorithm that is in the core of all MPEG and H.26x standards. However, this method involves motion estimation and compensation; thus the computational complexity is high. However, the video coding based on three-dimensional discrete cosine transform (3D DCT) is a potential method, and the scanning order and quantisation of 3D DCT coefficients play a crucial role in coding effect. So, the statistical performances of 3D DCT are studied in this paper, and then adaptive scanning order and quantisation of 3D coefficients are proposed. The theoretical analysis and experiment results show significant improvement in performance over previously reported methods.     

超低速率MELP语音编码算法研究

在语音编码算法中,混和激励线性预测(MELP)算法因为能更好的模拟自然语言特征,在低速率上能合成较高质量的语音,而成为现代低速率语音编码中最有潜力的算法之一。但在无线通信、卫星通信以及军用和保密通信中,信道带宽成为一个突出的问题,因此对更低速率语音压缩编码技术乃至超低速率的语音压缩编码技术的研究是非常有必要的。针对语音通信中关于极低速率的要求,深入分析了现今的几种基于MELP的低速率语音编码算法,对其原理以及关键技术进行了归纳总结,并对语音质量进行了比较。     

Image coding based on wavelet feature vector

In this article, an efficient image coding scheme that takes advantages of feature vector in wavelet domain is proposed. First, a multi‐stage discrete wavelet transform is applied on the image. Then, the wavelet feature vectors are extracted from the wavelet‐decomposed subimages by collecting the corresponding wavelet coefficients. And finally, the image is coded into bit‐stream by applying vector quantization (VQ) on the extracted wavelet feature vectors. In the encoder, the wavelet feature vectors are encoded with a codebook where the dimension of codeword is less than that of wavelet feature vector. By this way, the coding system can greatly improve its efficiency. However, to fully reconstruct the image, the received indexes in the decoder are decoded with a codebook where the dimension of codeword is the same as that of wavelet feature vector. Therefore, the quality of reconstructed images can be preserved well. The proposed scheme achieves good compression efficiency by the following three methods. (1) Using the correlation among wavelet coefficients. (2) Placing different emphasis on wavelet coefficients at different decomposing levels. (3) Preserving the most important information of the image by coding the lowest‐pass subimage individually. In our experiments, simulation results show that the proposed scheme outperforms the recent VQ‐based image coding schemes and wavelet‐based image coding techniques, respectively. Moreover, the proposed scheme is also suitable for very low bit rate image coding. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 123–130, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20045     

基于线性预测模型的氦语音增强算法研究

潜水员在水下工作时,由于生理方面的原因,需要以氦氧混合气体作为呼吸气体,由于气体结构的变化出现了氦语音现象,使得语音发生畸变,降低了清晰度。通过介绍基于线性预测模型的氦语音增强算法,将频域的线谱对(LSP)分析应用于氦语音增强中,由于线谱对系数与语音信号谱包络有紧密的联系,用线谱对参数(LSP)构成合成滤波器时更容易保证稳定性,所以提出了一种基于线谱对系数(LSP)的氦语音增强算法。通过实验将这种算法与基于线性预测LPC的增强算法进行了比较,实验结果表明,两种算法均能对氦语音进行矫正,并且新的算法能够对共振峰进行单独调节,在不影响清晰度的同时最大限度地保持了原有语音的细节,提高了可懂度。     

Switching between the SA‐DCT and the EI‐DCT in arbitrarily shaped image coding

In object‐oriented image coding applications, the use of the SA‐DCT (Shape‐Adaptive Discrete Cosine Transform) and of the block‐based DCT schemes associated to the EI (Extension‐Interpolation) padding technique have shown promising results at high and low bit rates, respectively. Both the SA‐DCT and the EI padding algorithm consist of two 1D‐DCT processing. Recent works have shown that their efficiencies can be further improved by the most appropriate selection of the first direction to be processed. This paper introduces novel methods to determine the preferential direction of processing boundary blocks of the SA‐DCT and of the EI padding technique. These methods are based on two known strategies related to the variances of the lengths of object segments in both directions, and a third one, which measures the energy compaction efficiency of the transforms. The proposed methods use the morphological feature TNOP (distribution of Texture according to the Number of Object Pixels) to adaptively select the most adequate strategy for each group of boundary blocks exhibiting similar number of object pixels. At last, it is introduced an adaptive switching scheme that selects between the proposed scheme for the block‐based DCT or for the SA‐DCT, when the available bit rate is known. This novel scheme allows different switching rules for distinct groups of boundary blocks, according to the feature TNOP. It outperforms the isolated use of both the SA‐DCT and the block‐based DCT schemes, at any specific bit rate. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 219–227, 2008; Published online in Wiley InterScience (www.interscience.wiley.com).     

基于位平面自适应算术编码的小波图像压缩技术

介绍了一种基于位平面上二进制算术编码的小波图像压缩技术。把算术编码方法应用于位平面层次,根据数据的重要性对静止图像经过小波变换以后的量化系数在相应的位平面的数据位进行高效的自适应压缩编码,有效地解决了难以进行概率统计的信源的编码问题。压缩效率高于其他编码方法。     

Dimensional scalable lossless compression of MRI images using haar wavelet lifting scheme with EBCOT

Modern medical imaging requires storage of large quantities of digitized clinical data. To provide high bandwidth and to reduce the storage space, a medical image must be compressed before transmission. One of the best image compression techniques is using the Haar wavelet transform. The method of discrete cosine transform (DCT) is chosen to be the preprocessing scheme to identify the image frequency information and has excellent energy compaction property. The block coding algorithm uses a wavelet transform to generate the sub band samples, which can be quantized and coded. It is more robust to errors than many other wavelet‐based schemes. In this article, simulations are carried out on different medical Images and it demonstrates the performance in terms of peak signal to noise ratio (PSNR) & bits per pixel (BPP). Our proposed method is found to preserve information fidelity while reducing the amount of data. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 175–181, 2014     

R-D匹配优选小波包零树图像压缩

为使R-D小波包优选算法与零树编码在量化精度上更好地匹配，对R-D小波包优选算法的寻优次序进行修改，使小波包各节点的量化精度差异不超过一位。实验结果表明，R-D匹配优选小波包零树编码可以取得与R—D优选小波包零树编码相当或比后者更好的图像压缩效果，可见R-D匹配优选算法是更适合零树编码的小波包优选算法。     

语音压缩编码技术研究进展

为了满足数字通信及其它商业应用的需求,语音压缩编码技术得到了迅速发展。介绍了目前语音压缩编码技术的研究进展,主要包括连续可变斜率增量调制（CVSD）、小波分析、多脉冲激励线性预测编码、散布脉冲码激励线性预测（DP-CELP）、多重脉冲散布非均匀代数码本激励线性预测（MPD-USACELP）、波形内插（WI）、线谱对（频率）（LSP）的量化。对以上算法进行了分析比较,总结了它们的特点和适用范围,并介绍了其中一些算法在DSP上的实时实现。