采用阶梯量化优化的SPECK算法研究

通过对图像小波变换系数的分析，根据SPECK算法对较高频子带上重要系数的编码问题，提出了一种采用阶梯量化优化的SPECK 算法QSPECK，以优化SPECK算法的编码效率。即先对小波系数矩阵的较高频子带进行阶梯量化，再对量化后的矩阵进行块编码，最后进行逐次逼近量化获得嵌入式码流。实验证明：此算法优化了编码效率，提高了PSNR值。     

基于几何方向的图像压缩算法

标准二维小波仅沿图像水平和垂直两个方向变换,图像稀疏化表示有待改进完善.文中提出了一种基于方向波变换( Directionlet)的图像压缩算法:通过八叉树分割算法对图像自适应分块;块内最优逼近凡何方向构成Directionlet变换的采样矩阵;块区域进行Directionlet变换,变换系数和方向信息分别编码.沿图像...     

一种基于二代曲波系数乘积的图象去噪方法

曲线波变换是一种多尺度变换,对于具有光滑曲线奇异性的目标函数,曲线波提供了稳定的、高效的和近于最优的表示.在第二代曲线波的基础上,利用曲线波分解中不同尺度的系数也具有相同的特点,提出了基于第二代曲波的系数乘积去噪算法.实验结果表明,提出的算法明显优于小波图像去噪方法,也优于曲线波的阈值方法.     

基于最优小波包变换和离散余弦变换的灰度图像水印算法

本文提出了一种基于最优小波包变换和离散余弦变换的灰度图像水印算法,利用Arnold变换和Baker映射对水印图像进行置乱,并将置乱结果进行分块,对宿主图像进行最优小波包分解,然后修改低频子带系数来嵌入水印信息.嵌入前,利用离散余弦变换去除低频子带相邻系数的相关性后嵌入经过同样处理的置乱水印子块.实验表明该算法在抵抗噪声、滤波、旋转剪切等攻击下有比较好的鲁棒性.     

基于小波域的第二代数字水印算法的研究

提出一种基于离散小波变换(DWT)域的第二代水印算法.该算法在CBWM模型基础上,把视觉系统掩蔽特性应用于水印的编码过程,引入了小波块的概念.算法首先从小波变换后的系数中提取出图像的重要特征值作为水印信息,然后通过调制将水印依次嵌入到图像的分解层中,最后该特征值在提取水印时作为位置参考点.实验结果表明,该水印算法对一般的几何攻击具有较强的鲁棒性,对旋转具有独特的抗干扰性.     

基于信息系数矩阵的多聚焦图像融合

提出了一个新的图像融合方法——基于信息系数矩阵的融合方法,首先对源图像进行离散小波框架变换,根据离散小波框架变换系数求取各图像中像素的清晰度指标,然后通过计算图像信息系数矩阵来决定在小波域当中如何选择一个像素值来完成最终的融合图像.仿真实验表明,该算法得到的融合图像具有良好的视觉效果和量化指标,体现出更强的融合性能.     

一种基于小波变换和矢量量化的图像压缩算法

小波变换和矢量量化都是图像压缩中的重要方法。利用小波变换的系数特点,对图像进行小渡分解,对于能量最为集中的低频分量采用标量量化处理,然后将标量量化过程中产生的残差和高频分量一起构造矢量,进行矢量量化。实验结果表明,此算法能够有效提高重构图像质量,获得较高的信噪比。     

零树框架下整数小波图像编码的改进

整数小波变换(Integer Wavelet Transform)有许多优点，但是图象经整数小波变换（IWT)后，能量集中性较第一代小波变换差很多，不利于嵌入式零树编码(Embedded Zerotree Wavelet Encoding)。因此本文提出一种新算法，从两方面加以改进。首先，采用“整数平方量化阈值选取算法”，根据整数小波变换后各子带系数幅值的动态变化较小，小波图像能量较一般小波差的特点，选取从1开始的正整数平方作为量化闽值的同时引入可调节的量化阈值系统，根据图像中不同区域的重要性选取与之相应的量化阈值，从而增加了零树的数量；其次，提出基于索引表和游程编码的小波零树编码的新思路，简化了编码与解码的过程。实验表明，本文算法充分的将整数小波变换与零树编码结合在一起，改善了压缩质量，提高了压缩效率。     

一种基于小波变换的高倍数SAR原始数据压缩算法

该文提出了一种基于分块提升小波变换的SAR原始数据压缩算法。在该算法中,针对SAR原始数据特点,提出一种有效的小波子带比特分配策略,为获得最优量化增益,在高比特率和低比特率两种情况下,分别采用均匀和非均匀Lloyd-Max量化器对小波系数进行量化。实验结果表明,该算法与传统BAQ和BAVQ算法相比,在信噪比和图像质量等各方面指标都取得了明显的改善。     

适合于高分辨力航测图像压缩的低复杂度算法

针对高分辨力航测图像数据量庞大的问题,提出了一种基于小波分析的低复杂度图像压缩算法.该算法首先对图像进行5级二维5/3小波变换,以去除图像像素之间的相关冗余.依据正交小波变换的子带变换增益对变换后的图像系数进行最佳量化以去除视觉冗余,最后针对小波变换后图像系数的概率分布特点对小波系数最低频子带LL采用一种基于上下文的预...     

Comparison between adaptive search and bit allocation algorithmsfor image compression using vector quantization

This article discusses bit allocation and adaptive search algorithms for mean-residual vector quantization (MRVQ) and multistage vector quantization (MSVQ). The adaptive search algorithm uses a buffer and a distortion threshold function to control the bit rate that is assigned to each input vector. It achieves a constant rate for the entire image but variable bit rate for each vector in the image. For a given codebook and several bit rates, we compare the performance between the optimal bit allocation and adaptive search algorithms. The results show that the performance of the adaptive search algorithm is only 0.20-0.53 dB worse than that of the optimal bit allocation algorithm, but the complexity of the adaptive search algorithm is much less than that of the optimal bit allocation algorithm.     

Joint Optimization of Run-Length Coding, Huffman Coding, and Quantization Table With Complete Baseline JPEG Decoder Compatibility

To maximize rate distortion performance while remaining faithful to the JPEG syntax, the joint optimization of the Huffman tables, quantization step sizes, and DCT indices of a JPEG encoder is investigated. Given Huffman tables and quantization step sizes, an efficient graph-based algorithm is first proposed to find the optimal DCT indices in the form of run-size pairs. Based on this graph-based algorithm, an iterative algorithm is then presented to jointly optimize run-length coding, Huffman coding, and quantization table selection. The proposed iterative algorithm not only results in a compressed bitstream completely compatible with existing JPEG and MPEG decoders, but is also computationally efficient. Furthermore, when tested over standard test images, it achieves the best JPEG compression results, to the extent that its own JPEG compression performance even exceeds the quoted PSNR results of some state-of-the-art wavelet-based image coders such as Shapiro's embedded zerotree wavelet algorithm at the common bit rates under comparison. Both the graph-based algorithm and the iterative algorithm can be applied to application areas such as web image acceleration, digital camera image compression, MPEG frame optimization, and transcoding, etc.      

一种基于最佳比特分配策略的三维小波视频编码新方法

对基于三维小波变换的视频编码进行了研究。由于视频图像传统的三维小波变换结构存在着诸多不足．文章提出了一种改进的三维小波变换结构。在给定的比特率条件下，通过对使得解码图像量化误差达到最小的最佳比特分配策略进行研究，给出了在均匀量化情况下的改进三维小波变换结构的量化步长。实验结果表明该方法的压缩性能明显优于传统方法。     

Embedded image coding using zerotrees of wavelet coefficients

The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code. The embedded code represents a sequence of binary decisions that distinguish an image from the “null” image. Using an embedded coding algorithm, an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly. Also, given a bit stream, the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream. In addition to producing a fully embedded bit stream, the EZW consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images. Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source. The EZW algorithm is based on four key concepts: (1) a discrete wavelet transform or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, (3) entropy-coded successive-approximation quantization, and (4) universal lossless data compression which is achieved via adaptive arithmetic 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.     

基于BWT和FVQ的极低比特率图像编码算法

该文提出了一种基于双正交小波变换(BWT)和模糊矢量量化(FVQ)的极低比特率图像编码算法。该算法通过构造符合图像小波变换系数特征的跨频带矢量,充分利用了不同频带小波系数之间的相关性,有效地提高了图像的编码效率和重构质量。该算法采用非线性插补矢量量化(NLIVQ)的思想,从大维数矢量中提取小维数的特征矢量,并提出了一种新的模糊矢量量化方法一渐进构造模糊聚类(PCFC)算法用于特征矢量的量化,从而大大提高了矢量量化的速度和码书质量。实验结果证明,该算法在比特率为0．172bpp的条件下仍能获得PSNR＞30dB的高质量重构图像。     

基于格的灰度级水印技术

提出一个新的灰度级水印算法．首先，使用量化技术对灰度级水印进行预处理，实现灰度级水印的数据压缩；然后，对原始图像进行小波分解，在小波域中使用格矢量量化技术构造水印的嵌人和提取算法．提取水印不需要原图像．为增强水印的安全性，使用混沌序列作为密钥对水印序列进行调制．实验结果表明，与同类算法相比该算法在获得较好感知质量含水印图像的同时提高了水印的鲁棒性．     

On color transforms and bit allocation for optimal subband image compression

Although subband transform coding is a useful approach to image compression and communication, the performance of this method has not been analyzed so far for color images, especially when the selection of color components is considered. Obviously, the RGB components are not suitable for such a compression method due to their high inter-color correlation. On the other hand, the common selection of YUV or YIQ is rather arbitrary and in most cases not optimal. In this work we introduce a rate–distortion model for color image compression and employ it to find the optimal color components and optimal bit allocation (optimal rates) for the compression. We show that the DCT (discrete cosine transform) can be used to transform the RGB components into an efficient set of color components suitable for subband coding. The optimal rates can be also used to design adaptive quantization tables in the coding stage with results superior to fixed quantization tables. Based on the presented results, our conclusion is that the new approach can improve presently available methods for color image compression and communication.     

Space-frequency quantization for wavelet image coding

A new class of image coding algorithms coupling standard scalar quantization of frequency coefficients with tree-structured quantization (related to spatial structures) has attracted wide attention because its good performance appears to confirm the promised efficiencies of hierarchical representation. This paper addresses the problem of how spatial quantization modes and standard scalar quantization can be applied in a jointly optimal fashion in an image coder. We consider zerotree quantization (zeroing out tree-structured sets of wavelet coefficients) and the simplest form of scalar quantization (a single common uniform scalar quantizer applied to all nonzeroed coefficients), and we formalize the problem of optimizing their joint application. We develop an image coding algorithm for solving the resulting optimization problem. Despite the basic form of the two quantizers considered, the resulting algorithm demonstrates coding performance that is competitive, often outperforming the very best coding algorithms in the literature.     

Weighted universal image compression

We describe a general coding strategy leading to a family of universal image compression systems designed to give good performance in applications where the statistics of the source to be compressed are not available at design time or vary over time or space. The basic approach considered uses a two-stage structure in which the single source code of traditional image compression systems is replaced with a family of codes designed to cover a large class of possible sources. To illustrate this approach, we consider the optimal design and use of two-stage codes containing collections of vector quantizers (weighted universal vector quantization), bit allocations for JPEG-style coding (weighted universal bit allocation), and transform codes (weighted universal transform coding). Further, we demonstrate the benefits to be gained from the inclusion of perceptual distortion measures and optimal parsing. The strategy yields two-stage codes that significantly outperform their single-stage predecessors. On a sequence of medical images, weighted universal vector quantization outperforms entropy coded vector quantization by over 9 dB. On the same data sequence, weighted universal bit allocation outperforms a JPEG-style code by over 2.5 dB. On a collection of mixed test and image data, weighted universal transform coding outperforms a single, data-optimized transform code (which gives performance almost identical to that of JPEG) by over 6 dB.