线性系统的稳定性与小波-分形子树量化编码的收敛性

 本文利用线性系统理论对Davis所采用的自子树量化(SQS)小波-分形变换图像编码算法进行了深入分析,发现编码过程实际上就是对动力系统的参数进行编码,并且SQS变换的吸引子与动力系统的稳定状态具有一致性.线性系统的观点表明序列和收敛性可以利用线性系统的稳定性理论来加以研究.于是根据线性控制理论的某些结论,我们提出了用系统的特征值来分析小波-分形变换编码的解码收敛性,同时给出了收敛的充要条件以及终值压缩收敛的充分条件.     

基于小波变换和四叉树的图像分形编码算法研究

提出利用小波变换和分形编码的优势,进行图像压缩编码.最低分辨率子带进行失真较小的均匀量化编码.对高分辨率子带设定系数阈值,小波系数小于等于系数阈值则直接置零;大于系数阈值的块采用四叉树算法进行分形编码,如果误差小于等于误差阈值,则记录分形编码参数,如果误差大于误差阈值,则进行四叉树分裂.对算法进行了matlab仿真,实验表明解码图像质量和运算速度都有较大改善.     

基于变换域Wyner-Ziv视频编码的有效重建算法

为了减少分布式视频编码系统重建值与真实值之间误差,提出了基于变换域Wyner-Ziv视频编码最小均方误差(MMSE)重建的一种有效重建算法.该算法充分利用视频帧间相关性,对MMSE重建算法积分区间做出调整,当边信处于解码值对应量化区间之内时,在量化区闻内利用MMSE重建;当边信息处于解码值对应量化区间之外时,对量化区间做出调整,在改进后的区间利用MMSE重建.实验结果表明,与最佳重建最小均方误差重建算法相比,该算法可以有效提高解码视频的平均PSNR.     

一种基于变换域Wyner-Ziv视频编码的有效重建算法

为了减少分布式视频编码系统解码后重建值与真实值之间误差,提出了基于变换域Wyner-Ziv视频编码最小均方误差(MMSE)重建的一种新的有效重建算法。该算法充分利用视频帧间相关性,对MMSE重建算法积分区间做出调整,当边信处于解码值对应量化区间之内时,在量化区间内利用MMSE重建;当边信息处于解码值对应量化区间之外时,对量化区间做出调整,在改进后的区间利用MMSE重建。实验结果表明,与最佳重建最小均方误差重建算法相比,该算法可以有效提高解码视频的平均PSNR。     

一种基于Wyner-Ziv结构的贝尔模板图像编码方法

该文提出一种基于Wyner-Ziv(WZ)结构的四通道贝尔模板图像分布式编解码方法。在编码端对贝尔模板图像进行结构分离转换,形成4个分量图像并分别执行离散余弦变换,依据拉格朗日代价函数的收敛性,利用Lloyd迭代算法设计了全局优化的量化器,采用Slepian-Wolf(SW)信道编码方法对各分量变换系数的量化输出进行独立编码,在解码端利用亮度分量作为边信息,联合解码重构贝尔模板图像。实验表明,在高速率情况下,其率失真性能得到较好的改善。     

无反馈分布式视频编码中Wyner-Ziv帧的顽健重构算法

在无反馈分布式视频编码系统中,提出了一种Wyner-Ziv帧的顽健重构算法。针对比特面解码错误带来的视频质量下降问题,对DC系数和AC系数使用不同重构方法,特别是对于解码失败的DC系数量化值,利用编码端原始图像的相关信息自适应地调整边信息量化值和解码失败量化值对重构的贡献,从而完成重构。实验结果表明,与最小均方误差重构算法相比,该算法可以有效提高解码视频的平均PSNR(peak signal-to-noise ratio),且解码视频图像的主观质量有明显改善。     

一种应用于指纹识别系统的指纹图像压缩算法

针对应用于指纹识别系统中指纹图像的压缩编码问题,提出了一种改进的基于四叉树分类的网格编码量化(QTCQ)的指纹图像压缩算法.该算法对小波变换后的高频系数采用2×2的DCT变换进一步集中能量,并对变换后的系数进行系数重排以使得高频子带内的重要系数集中于相应子带的低频位置,再通过基于四叉树的网格编码量化进行量化编码.仿真结果表明,该算法比WSQ和JPEG2000等均具有更好的压缩性能.     

基于量化系数均方误差准则的运动估计算法

提出了一种基于量化系数均方误差匹配准则的DCT域运动估计视频编码算法.算法中采用了一种新的运动估计匹配准则,该准则在DCT域内计算逆量化的残差均方误差值.由于该准则已考虑到量化噪声对运动残差能量的影响,因此与传统编码算法相比较,在图像质量基本不变的前提下码率更低.仿真结果显示,基于量化系数均方误差准则的DCT域运动估计算法具有较高的编码效率.     

第二代Curvelet变换域的信息隐藏方法

提出了一种基于第二代Curvelet变换的信息隐藏方法。该方法以数字图像作为掩体介质,根据Curvelet子带内各向异性尺度关系选择部分系数,并对其实部和虚部分别进行量化嵌入。引入了误差控制编码以减少误码,在提取秘密信息时不需要参考原掩体介质,实现了密文的盲提取。与DCT域Jsteg算法和小波域量化索引调制算法进行比较,结果表明,提出的算法具有较高图像质量和更大的嵌入容量。     

基于非抽样Contourlet变换的自适应阈值图像增强算法

提出了一种基于非抽样Contourlet变换的自适应阈值图像增强算法,首先对图像进行非抽样Contourlet变换得到不同尺度不同方向上的变换系数,然后由变换系数自适应地确定阈值和调整增强函数,并对变换系数做增强处理,最后对增强处理后的变换系数进行反变换,实现图像增强.实验结果表明,与其他基于变换域的算法相比,该算法可以得到更好的增强效果.     

小波方向子树的分形量化与零树量化相结合

本文通过对标准图像内在相关性的统计分析得出了自然图像具有方向自仿射性的结论,并首次提出了小波方向子树量化的概念。对分别属于水平、垂直和对角方向的尺度压缩因子是相互独立的情况采用小波方向子树量化进行了编/解码分析,同时深入研究了零树小波量化与小波方向子树量化之间的相互关系。实验证明,采用小波方向子树量化与零树量化的混合编码算法可使编码质量有较大的改善。     

Improved MPEG-4 still texture image coding under noisy environment

This paper describes the performance of the MPEG-4 still texture image codec in coding noisy images. As will be shown, when using the MPEG-4 still texture image codec to compress a noisy image, increasing the compression rate does not necessarily imply reducing the peak-signal-to-noise ratio (PSNR) of the decoded image. An optimal operating point having the highest PSNR can be obtained within the low bit rate region. Nevertheless, the visual quality of the decoded noisy image at this optimal operating point is greatly degraded by the so-called "cross" shape artifact. In this paper, we analyze the reason for the existence of the optimal operating point and the "cross" shape artifact when using the MPEG-4 still texture image codec to compress noisy images. We then propose an adaptive thresholding technique to remove the "cross" shape artifact of the decoded images. It requires only a slight modification to the quantization process of the traditional MPEG-4 encoder while the decoder remains unchanged. Finally, an analytical study is performed for the selection and validation of the threshold value used in the adaptive thresholding technique. It is shown that, the visual quality and PSNR of the decoded images are much improved by using the proposed technique comparing with the traditional MPEG-4 still texture image codec in coding noisy images.     

A graph-theoretic approach for studying the convergence of fractalencoding algorithm

We present a graph-theoretic interpretation of convergence of fractal encoding based on partial iterated function system (PIFS). First we have considered a special circumstance, where no spatial contraction has been allowed in the encoding process. The concept leads to the development of a linear time fast decoding algorithm from the compressed image. This concept is extended for the general scheme of fractal compression allowing spatial contraction (on averaging) from larger domains to smaller ranges. A linear time fast decoding algorithm is also proposed in this situation, which produces a decoded image very close to the result obtained by an ordinary iterative decompression algorithm.     

Rate bounds on SSIM index of quantized images

In this paper, we derive bounds on the structural similarity (SSIM) index as a function of quantization rate for fixed-rate uniform quantization of image discrete cosine transform (DCT) coefficients under the high-rate assumption. The space domain SSIM index is first expressed in terms of the DCT coefficients of the space domain vectors. The transform domain SSIM index is then used to derive bounds on the average SSIM index as a function of quantization rate for uniform, Gaussian, and Laplacian sources. As an illustrative example, uniform quantization of the DCT coefficients of natural images is considered. We show that the SSIM index between the reference and quantized images fall within the bounds for a large set of natural images. Further, we show using a simple example that the proposed bounds could be very useful for rate allocation problems in practical image and video coding applications.     

Discrete spectral phase coding (Corresp.)

A novel approach to discrete signal encoding is presented. This approach is based on techniques employed in digital holography for the computation of phase-only holograms [1]. The decoded sequence exhibits a burst error immunity which is characteristic of holographic reproductions. Also, a nonrandom characterization of quantization error is presented that indicates a decoding procedure for reducing the quantization error in the decoded sequence.     

Performance of Block Cosine Image Coding with Adaptive Quantization

Quantizers for block transform image coding systems are typically designed under the assumption of Gaussian statistics for the transform coefficients. While convincing arguments can be provided in support of this approach, empirical evidence is presented demonstrating that, except possibly for the dc term, wide departures from Gaussian behavior can be expected for real-world imagery at typical block sizes. In this paper we describe the performance of a block cosine image coding system with an adaptive quantizer matched to the statistics of the transform coefficients. The adaptive quantizer is based upon a recently developed algorithm which employs a training sequence in the design procedure. At encoding rates of approximately 1 bit/pixel and above, this approach results in significant improvement in reconstructed image quality compared to fixed quantization schemes designed under the Gaussian assumption. For rates much below 1 bit/pixel the relative improvement is negligible.     

Image sharpening in the JPEG domain

We present a new technique for sharpening compressed images in the discrete-cosine-transform domain. For images compressed using the JPEG standard, image sharpening is achieved by suitably scaling each element of the encoding quantization table to enhance the high-frequency characteristics of the image. The modified version of the encoding table is then transmitted in lieu of the original. Experimental results with scanned images show improved text and image quality with no additional computation cost and without affecting compressibility.     

内插法在提高电视图像清晰度中的应用

在图像信息的编码过程中,由于量化精度不高造成高频成分的损失导致图像不清晰。通过内插的方法增加电视图像的像素数,把隔行扫描改造成逐行扫描,达到提高图像清晰度的目的。     

基于自适应下采样和超分重建的图像压缩框架

针对联合图像专家组(JPEG)标准设计了一种基于自适应下采样和超分辨力重建的图像压缩编码框架。在编码器端,为待编码的原始图像设计了多种不同的下采样模式和量化模式,通过率失真优化算法从多种模式中选择最优的下采样模式(DSM)和量化模式(QM),最后待编码图像将在选择的模式下进行下采样和JPEG编码;在解码器端,采用基于卷积神经网络的超分辨力重建算法对解码后的下采样图像进行重建。此外,所提出的框架扩展到JPEG2000压缩标准下同样有效可行。仿真实验结果表明,相比于主流的编解码标准和先进的编解码方法,提出的框架能有效地提升编码图像的率失真性能,并能获得更好的视觉效果。