基于小波零树的图像压缩

在信号处理领域，利用小波变换进行多分辨率分解是非常实用有效的方法。由于表现一个图像最主要的部分是低频部分，因此利用小波在时、频域良好的局部定位特性，在金字塔结构小波变换的基础上对信号进行多分辨率分解，提取其低频成分进行压缩，就可以得到合适的具有一定压缩比的处理图像。再将压缩后信号进行阈值量化编码，就可以得到更高质量和压缩比的图像。     

EBCOT双上下文窗口并行编码及FPGA实现

JPEG2000编码系统中，EBCOT的编码速度已经成为整个系统编码效率的瓶颈。通过研究EBCOT编码原理和通道并行算法的编码过程，提出了双上下文窗口位并行的EBCOT系数位建模方法。详细说明了使用该算法的系数位建模系统的硬件结构。系数位编码系统有效减少了编码时钟周期数，并在FPGA上进行了功能验证。     

小波分析在电气数据消噪与压缩中的应用

介绍了小波分析的基本概念，小波变换用于信号压缩和消噪的基本理论和算法。分析了电器采集数据的特点。并针对该特点提出了一种基于分解系数最大值的阈值选取方法，确定最佳小波函数的选取，并给出了仿真实例，证明了本方法的实用性。井计算了采用不同小波函数以度不同阈值选取方法时信号的压缩比。比较了压缩和消噪效果的不同。     

一种静止图像压缩的新方法

图像压缩是当今信息时代研究的热点之一,用比较成熟的小波变换技术压缩图像也难以满足高图像压缩比的要求,利用正交样条函数,提出一种有别于小波分析的多分辨率理论,把它用于静止图像压缩,并在VisualC#环境下用该理论做了静止图像压缩实验,选取的图像是经典的标准侧视图Lena、Baboon和Barbara,压缩比分别达到1.7146、1.2398和1.5517,结果同吉林大学姜岩的硕士毕业论文的结果进行了比较,结果表明,相对于小波分析,利用正交样条函数进行图像变换压缩,在无损压缩方面的压缩量更大,效果更好。     

基于小波分析的多分辨率地形构建

地形建模是虚拟战场构建的一个重要方面,DEM和正射影像是地形可视化的主要数据来源,他们可看作是表示地形信息的二维信号。首先论述了小波分析的基本原理,利用多次二进制小波对DEM和影像进行压缩,构建了一个多分辨率地形模型。实践证明,该方法具有压缩比高、压缩速度快等优点,同时还解决了不同分辨率子块之间的"裂缝"问题,可以满足大规模地形数据实时无缝漫游的需要。     

小波域BP网络图像压缩及Matlab实现

BP网络是一种非线性映射网络,三层BP网络可以很好地逼近任一连续函数.BP网络用于图像压缩是一个很好的创意.但因其训练时间过长,而没有得到应有的重视.人的视觉特性对图像的低频部分及图像的边缘部分比较敏感.经小波分解后图像的大部分能量集中在低频和图像的边缘部分,即这部分小波系数比较大,其余部分小波系数很小,接近于零.因此提出了小波域BP网络图像压缩方法.该方法是根据小波变换后小波区域重要性的不同而采用不同的压缩比.即不重要的小波系数采用大压缩比的BP网络(隐节点少),重要的系数采用小压缩比网络(隐节点多)或不进行BP压缩而直接编码.并给出Matlab仿真程序.     

基于小波变换的视频压缩的硬件实现

介绍了一种基于小波变换的换频压缩系统方案,其算法简单、计算量小,易于硬件实现。并着重分析了其自适应量化的实现原理,给出系统结构。实验数据表明该系统支持多分辨率,压缩比范围大,压缩视频质量稳定。     

一种基于小波变换的混合编码方法研究

将小波理论应用于视频和图像压缩是目前国内外十分重视的图像压缩技术。小波分析技术改善了基于分块DCT变换的压缩编码技术的方块效应和飞蚊噪声的不足：而分形压缩编码方法．由于可以获得极高的压缩比而得到广泛关注。本文在借鉴前人的图像压缩编码算法的基础上,提出了一种图像压缩混合编码方法：该方法首先将图像进行小波分解,然后对不同的频带特征采用不同量化、压缩方法进行编码。实践表明,该方法不仅能够大大提高图像的压缩比,而且在网络信道不稳定的情况下．能在一定程度上克服数据传输出错．从而避免数据丢失造成的恢复图像质量下降。     

基于免疫克隆选择的图像压缩算法研究

针对在图像压缩过程中压缩质量与压缩时间相互间存在矛盾,文中提出了一种基于免疫克隆选择的图像压缩算法。该算法结合免疫克隆算法的抗体的多样性、全局寻优和小波变换多分辨率分析的特点。在压缩过程中不仅采用了小波分解后的低频分量,还加入了一定的高频部分,使压缩后的图像信噪比更低,从而保证了图像的质量。实验结果表明,所提出的图像压缩方法具有相对较高的压缩比和良好的压缩效果。     

图像小波变换的分形编码技术

简要介绍小波变换在图像处理上的基本原理，初步探讨小波变换的分区块编码算法，在小波分解的基础上结合人眼特性，利用子带的相关性，引进分形编码思想，对图像进行有效压缩。仿真实验结果说明，该方法在同等图像的恢复质量下，其压缩比优于传统的压缩方法，在提高图像的恢复质量方面也有一定价值。     

Lossless compression of color mosaic images.

Lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The lossless compression performance of the proposed method on color mosaic images is apparently the best so far among the existing lossless image codecs.     

SPIHT静止图像压缩技术研究

JPEG2000的静止图像压缩算法具有很多优良特性,其核心算法是EBCOT,构造复杂、硬件实现难度大。而基于SPIHT算法的图像压缩效率接近EBCOT,但结构简单、易于硬件实现。通过对SPIHT和JPEG2000算法进行融合,提出了一套压缩比高、可实现由有损到无损、码流渐进传输的静止图像编码方案,并对SPIHT算法与(9,7)小波提升算法的融合方法进行分析研究,所构造系统性能与JPEG2000算法接近,具有较好的应用前景。     

Lossless image compression with projection-based and adaptive reversible integer wavelet transforms

Reversible integer wavelet transforms are increasingly popular in lossless image compression, as evidenced by their use in the recently developed JPEG2000 image coding standard. In this paper, a projection-based technique is presented for decreasing the first-order entropy of transform coefficients and improving the lossless compression performance of reversible integer wavelet transforms. The projection technique is developed and used to predict a wavelet transform coefficient as a linear combination of other wavelet transform coefficients. It yields optimal fixed prediction steps for lifting-based wavelet transforms and unifies many wavelet-based lossless image compression results found in the literature. Additionally, the projection technique is used in an adaptive prediction scheme that varies the final prediction step of the lifting-based transform based on a modeling context. Compared to current fixed and adaptive lifting-based transforms, the projection technique produces improved reversible integer wavelet transforms with superior lossless compression performance. It also provides a generalized framework that explains and unifies many previous results in wavelet-based lossless image compression.     

JPEG2000 compression coding using human visual system model

In order to apply the Human Visual System (HVS) model to JPEG2000 standard, several implementation alternatives are discussed and a new scheme of visual optimization is introduced with modifying the slope of rate-distortion. The novelty is that the method of visual weighting is not lifting the coefficients in wavelet domain, but is complemented by code stream organization. It remains all the features of Embedded Block Coding with Optimized Truncation (EBCOT) such as resolution progressive, good robust for error bit spread and compatibility of lossless compression. Well performed than other methods, it keeps the shortest standard codestream and decompression time and owns the ability of Visual Progressive (VIP) coding.     

Wavelet image coding using variable blocksize vector quantization with optimal quadtree segmentation

In this paper, we propose an image coding scheme by using the variable blocksize vector quantization (VBVQ) to compress wavelet coefficients of an image. The scheme is capable of finding an optimal quadtree segmentation of wavelet coefficients of an image for VBVQ subject to a given bit budget, such that the total distortion of quantized wavelet coefficients is minimal. From our simulation results, we can see that our proposed coding scheme has higher performance in PSNR than other wavelet/VQ or subband/VQ coding schemes.     

Lossless image compression based on optimal prediction, adaptivelifting, and conditional arithmetic coding

The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied for the lossless compression of still images using first quincunx sampling and then simple row-column sampling. In each case, the efficiency of the linear predictors is enhanced nonlinearly. Directional postprocessing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely efficient image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet coefficients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding.     

Symmetry-Based Scalable Lossless Compression of 3D Medical Image Data

We propose a novel symmetry-based technique for scalable lossless compression of 3D medical image data. The proposed method employs the 2D integer wavelet transform to decorrelate the data and an intraband prediction method to reduce the energy of the sub-bands by exploiting the anatomical symmetries typically present in structural medical images. A modified version of the embedded block coder with optimized truncation (EBCOT), tailored according to the characteristics of the data, encodes the residual data generated after prediction to provide resolution and quality scalability. Performance evaluations on a wide range of real 3D medical images show an average improvement of 15% in lossless compression ratios when compared to other state-of-the art lossless compression methods that also provide resolution and quality scalability including 3D-JPEG2000, JPEG2000, and H.264/AVC intra-coding.      

Visually improved image compression by combining a conventional wavelet-codec with texture modeling

Human observers are very sensitive to a loss of image texture in photo-realistic images. For example a portrait image without the fine skin texture appears unnatural. Once the image is decomposed by a wavelet transformation, this texture is represented by many wavelet coefficients of low- and medium-amplitude. The conventional encoding of all these coefficients is very bitrate expensive. Instead, such an unstructured or stochastic texture can be modeled by a noise process and be characterized with very few parameters. Thus, a hybrid scheme can be designed that encodes the structural image information by a conventional wavelet codec and the stochastic texture in a model-based manner. Such a scheme, called WITCH (Wavelet-based Image/Texture Coding Hybrid), is proposed. It implements such an hybrid coding approach, while nevertheless preserving the features of progressive and lossless coding. Its low computational complexity and the parameter coding costs of only 0.01 bpp make it a valuable extension of conventional codecs. A comparison with the JPEG2000 image compression standard showed that the WITCH-scheme achieves the same subjective quality while increasing the compression ratio by more than a factor of two.     

Wavelet-based lossless compression of coronary angiographic images

The final diagnosis in coronary angiography has to be performed on a large set of original images. Therefore, lossless compression schemes play a key role in medical database management and telediagnosis applications. This paper proposes a wavelet-based compression scheme that is able to operate in the lossless mode. The quantization module implements a new way of coding of the wavelet coefficients that is more effective than the classical zerotree coding. The experimental results obtained on a set of 20 angiograms show that the algorithm outperforms the embedded zerotree coder, combined with the integer wavelet transform, by 0.38 bpp, the set partitioning coder by 0.21 bpp, and the lossless JPEG coder by 0.71 bpp. The scheme is a good candidate for radiological applications such as teleradiology and picture archiving and communications systems (PACS's).     

一种新的二维非线性提升小波变换方法

根据图像的统计信息,本文构造了一种新的非线性算子即统计算子,提出了基于该算子的一种新的非线性提升小波分析方法.使图像经过该方法变换以后,在无量化失真的前提下,以较大概率取得零高频系数.本文将该方法与现存文献中所提出的非线性形态学小波等分析方法,进行了标准图像的测试分析,实验结果显示,利用本文所提出的基于统计算子的提升小波分析的方法所得到的高频子带的熵都低于其它几种非线性小波变换,取得了很好的分析结果.