基于多级零树编码的小波系数网格编码量化

该文提出了对小波图像作多级零树编码后进行网格编码量化（TCQ）的新方法。首先利用子带间的相关性进行零树编码，然后利用卷积编码和信号空间扩展来增大量化信号间的欧氏距离，并用Viterbi算法寻找最优量化序列。仿真结果表明，该方法比零树编码后采用最优量化要提高0．3dB左右。该方法还具有编码计算复杂度适中，解码简单的优点。     

Efficient entropy estimation based on doubly stochastic models for quantized wavelet image data.

Under a rate constraint, wavelet-based image coding involves strategic discarding of information such that the remaining data can be described with a given amount of rate. In a practical coding system, this task requires knowledge of the relationship between quantization step size and compressed rate for each group of wavelet coefficients, the R-Q curve. A common approach to this problem is to fit each subband with a scalar probability distribution and compute entropy estimates based on the model. This approach is not effective at rates below 1.0 bits-per-pixel because the distributions of quantized data do not reflect the dependencies in coefficient magnitudes. These dependencies can be addressed with doubly stochastic models, which have been previously proposed to characterize more localized behavior, though there are tradeoffs between storage, computation time, and accuracy. Using a doubly stochastic generalized Gaussian model, it is demonstrated that the relationship between step size and rate is accurately described by a low degree polynomial in the logarithm of the step size. Based on this observation, an entropy estimation scheme is presented which offers an excellent tradeoff between speed and accuracy; after a simple data-gathering step, estimates are computed instantaneously by evaluating a single polynomial for each group of wavelet coefficients quantized with the same step size. These estimates are on average within 3% of a desired target rate for several of state-of-the-art coders.     

基于三维子波变换和分级零树扫描的视频编码算法研究

本文研究了三维子波变换和分级三维零树混合视频编码算法,对图像序列体作３层２２子带时,空分解后,再对最低频子带进行４子带空间分解,对这个特殊分解的三维子波结构,推广了零树的概念,用ＨＶＳ量化模型对子波系统施加量化,用分级三维零树表示量化后的２５带子波系数,这一方案比单级零树方法节省约６％的码率,并且实现了空间和时间可尺主度化码流结构。     

图像小波系数的分布规律及其对图像质量的影响

文中研究了小波系数的统计特性,小波系数的均值、方差和能量,不同分辨率级相同方向上小小的关系,以及同一分辨率级不同方向的小波系数对图像主观质量的影响。从而为基于小波变换的图像数据压缩中的量化方法提供了依据,得到以下结论：１．能量主要集中在低频系数,它对图像质量的影响最大,应对其进行量化;２．高频系数代表图像的细节信息,分辨率级越高的高频系数,对图像质量的影响越小,量化可以越粗;３．对同一方向不同分辨     

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。     

Hierarchical partition priority wavelet image compression

Image compression methods for progressive transmission using optimal hierarchical decomposition, partition priority coding (PPC), and multiple distribution entropy coding (MDEC) are presented. In the proposed coder, a hierarchical subband/wavelet decomposition transforms the original image. The analysis filter banks are selected to maximize the reproduction fidelity in each stage of progressive image transmission. An efficient triple-state differential pulse code modulation (DPCM) method is applied to the smoothed subband coefficients, and the corresponding prediction error is Lloyd-Max quantized. Such a quantizer is also designed to fit the characteristics of the detail transform coefficients in each subband, which are then coded using novel hierarchical PPC (HPPC) and predictive HPPC (PHPPC) algorithms. More specifically, given a suitable partitioning of their absolute range, the quantized detail coefficients are ordered based on both their decomposition level and partition and then are coded along with the corresponding address map. Space filling scanning further reduces the coding cost by providing a highly spatially correlated address map of the coefficients in each PPC partition. Finally, adaptive MDEC is applied to both the DPCM and HPPC/PHPPC outputs by considering a division of the source (quantized coefficients) into multiple subsources and adaptive arithmetic coding based on their corresponding histograms. Experimental results demonstrate the great performance of the proposed compression methods.     

Scalable wavelet video coding using aliasing-reduced hierarchicalmotion compensation

We describe a spatially scalable video coding framework in which motion correspondences between successive video frames are exploited in the wavelet transform domain. The basic motivation for our coder is that motion fields are typically smooth and, therefore, can be efficiently captured through a multiresolutional framework. A wavelet decomposition is applied to each video frame and the coefficients at each level are predicted from the coarser level through backward motion compensation. To remove the aliasing effects caused by downsampling in the transform, a special interpolation filter is designed with the weighted aliasing energy as part of the optimization goal, and motion estimation is carried out with low pass filtering and interpolation in the estimation loop. Further, to achieve robust motion estimation against quantization noise, we propose a novel backward/forward hybrid motion compensation scheme, and a tree structured dynamic programming algorithm to optimize the backward/forward mode choices. A novel adaptive quantization scheme is applied to code the motion predicted residue wavelet coefficients, Experimental results reveal 0.3-2-dB increase in coded PSNR at low bit rates over the state-of-the-art H.263 standard with all enhancement modes enabled, and similar improvements over MPEG-2 at high bit rates, with a considerable improvement in subjective reconstruction quality, while simultaneously supporting a scalable representation.     

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.     

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.     

A successive approximation vector quantizer for wavelet transformimage coding

A coding method for wavelet coefficients of images using vector quantization, called successive approximation vector quantization (SA-W-VQ) is proposed. In this method, each vector is coded by a series of vectors of decreasing magnitudes until a certain distortion level is reached. The successive approximation using vectors is analyzed, and conditions for convergence are derived. It is shown that lattice codebooks are an efficient tool for meeting these conditions without the need for very large codebooks. Regular lattices offer the extra advantage of fast encoding algorithms. In SA-W-VQ, distortion equalization of the wavelet coefficients can be achieved together with high compression ratio and precise bit-rate control. The performance of SA-W-VQ for still image coding is compared against some of the most successful image coding systems reported in the literature. The comparison shows that SA-W-VQ performs remarkably well at several bit rates and in various test images.     

基于方向树结构矢量分类的小波图像网格编码矢量量化

本文提出了采用方向树结构矢量组合并分类对小波图像进行网格编码矢量量化（ＴＣＶＱ）的新方法。该方法矢量构成结合了子带系数的方向性，充分利用了子带系数带间和带内相关性，按能量和活跃度进行两级分类，降低了类中矢量的内部离散度，对活跃和非活跃类矢量实行加权ＴＣＶＱ，利用卷积编码扩展信号空间，用维特比算法搜索最优量化序列，比使用加权 ＶＱ提高了 ０．７ｄｂ左右。该方法编码计算复杂度适中，解码简单，有较好的压缩效果。     

Image coding based on wavelet transform and uniform scalar dead zone quantizer

In this paper, a new wavelet transform image coding algorithm is presented. The discrete wavelet transform (DWT) is applied to the original image. The DWT coefficients are firstly quantized with a uniform scalar dead zone quantizer. Then the quantized coefficients are decomposed into four symbol streams: a binary significance map symbol stream, a binary sign stream, a position of the most significant bit (PMSB) symbol stream and a residual bit stream. An adaptive arithmetic coder with different context models is employed for the entropy coding of these symbol streams. Experimental results show that the compression performance of the proposed coding algorithm is competitive to other wavelet-based image coding algorithms reported in the literature.     

结合相干斑抑制的小波域SAR图像网格编码量化

本文结合SAR图像相干斑的抑制,研究了一种在小波域对带噪SAR图像做网格编码量化的新方法。首先将SAR图像在小波域内实施软阀值去噪声,然后根据SAR图像在小波域中各子带系数固有的树结构关系对其进行零树分类,对分类后的重要性小波系数进行网格编码量化,利用卷积编码和信号空间扩展来增大量化信号间的欧氏距离,并用维特比算法寻找最优量化序列。该方法综合了小波相干斑抑制、零树编码、网格编码量化技术,不仅利用了信号小波变换域的空间相关性,而且也较好地利用了信号间的时间相关性。在压缩的同时进行了相干斑抑制,在SAR图像的压缩中取得了很好的效果。     

Design of multiplierless, high-performance, wavelet filter banks with image compression applications

The JPEG2000 image coding standard employs the biorthogonal 9/7 wavelet for lossy compression. The performance of a hardware implementation of the 9/7 filter bank depends on the accuracy and the efficiency with which the quantized filter coefficients are represented. A high-precision representation ensures compression performance close to the unquantized, infinite precision filter bank, but at the cost of increased hardware resources and processing time. If the filter coefficients are quantized such that the filter bank properties are preserved, then, the degradation in compression performance will be minimal. This paper investigates two filter structures and two "compensating" filter coefficient quantization methods for improving the performance of multiplierless, quantized filter banks. Rather than using an optimization technique to guide the design process, the new methods utilizes the perfect reconstruction requirements of the filter bank. The results indicate that the best method (a cascade structure with compensating zeros) realizes image-compression performance very similar to the unquantized filter case while also achieving a fast, efficient hardware implementation.     

Image subband coding using context-based classification andadaptive quantization

Adaptive compression methods have been a key component of many proposed subband (or wavelet) image coding techniques. This paper deals with a particular type of adaptive subband image coding where we focus on the image coder's ability to adjust itself "on the fly" to the spatially varying statistical nature of image contents. This backward adaptation is distinguished from more frequently used forward adaptation in that forward adaptation selects the best operating parameters from a predesigned set and thus uses considerable amount of side information in order for the encoder and the decoder to operate with the same parameters. Specifically, we present backward adaptive quantization using a new context-based classification technique which classifies each subband coefficient based on the surrounding quantized coefficients. We couple this classification with online parametric adaptation of the quantizer applied to each class. A simple uniform threshold quantizer is employed as the baseline quantizer for which adaptation is achieved. Our subband image coder based on the proposed adaptive classification quantization idea exhibits excellent rate-distortion performance, in particular at very low rates. For popular test images, it is comparable or superior to most of the state-of-the-art coders in the literature.     

Scalable video compression via overcomplete motion compensated wavelet coding

Recently, there have been a flurry of works on overcomplete motion compensated wavelet coding (OMCWC). In this paper, we address the importance of phase and focus on the design of scalable video coding algorithms within the OMCWC framework. Specifically, our new contributions consist of the following three components: (1) efficient block motion estimation techniques in the wavelet domain including hierarchical and fractional-pel block matching, (2) extend overcomplete motion compensated prediction (MCP) into overcomplete motion compensated temporal filtering (MCTF) to achieve temporal scalability (3) context modeling strategies for embedded quantization and entropy coding of 3D wavelet coefficients. Experiment results are used to demonstrate that the class of overomplete MCP/MCTF coders are capable of achieving comparable performance to other competing interframe wavelet coders.     

基于遗传算法与模糊聚类相结合的小波变换矢量量化方法

提出一种有效的图像压缩方法，利用小波变换对图像进行多分辨率分解，对小波系数进行矢量量化(VQ)编码。使用遗传算法(GA)与模糊c均值聚类(FCM)算法相结合的方法来设计码书，有效地克服了FCM算法容易陷入局部最优且对初始值敏感的缺点。实验结果表明，该算法可较大提高图像重构质量。     

差错信道下基于框架扩展的高效多描述图像编码

由量化框架扩展系数所产生的多描述图像编码,在差错信道下具有良好的图像恢复性能。该文提出一种基于框架扩展的高效多描述图像编码。首先构造一种在量化噪声下具有均方误差意义上最优框架重建的均匀紧支框架。然后以小波零树作为框架扩展的基本运算单元,在小波域对不同子带信息采用不同维度的框架进行扩展。通过不同维度的框架扩展实现对不同子带信息的不同保护。框架扩展后的数据经量化编码形成多描述码流。实验结果证明,相较于其他几种基于框架扩展的方法,该文方法在同样的信道条件下具有更好的图像差错恢复性能。     

基于小波基优化和动态比特分配的数字音频压缩编码方案

提出了一种新颖的基于自适应小波基优化选择和心理声学模型相结合的数字音频信号的透明质量编码方法，保证固定失真水平上使每帧信号的变换系数的动态分配的比特数最少，并且利用动态码本的方法来消除音频信号的统计冗余，进一步压缩比特率，对于抽样率为４４．１ｋＨｚ每样值用１６比特线性码表示的光盘单声道音乐信号可以压缩到６４ｋＢＰＳ左右。     

一种基于小波系数重要图编码的图像压缩算法

提出了一种新的基于小波系数重要图编码的图像压缩算法。该算法根据量化后的波系数的特点进行了一种期望排序，然后舍掉序列后面大量的零值小波系数，从而得到一个波系数子集，能以少的小波系数来很好地逼近原始图像，省去了零树编码中零树结构带来的大量比特开销。实验表明，该算法与MPEG-4的静止图像压缩算法相比较，重构图像的峰值信噪比（PSNR）值在相同码率下有较大的提高。