小波与分形相结合的图像编码方法

本文介绍了一种小波与分形相结合的混合图像编码方法。对图像的高频部分运用分形编码。对图像的低频部分运用小波编码。实验证明取得了较好的效果。     

采用分形与小波相结合的新型图像压缩方法

提出的这一方法是根据人眼观察事物的特点加以设计的,即先观察事物整体印象（图像平滑区域）,然后着眼于细节部分（图像边缘区域）,具体方法是先对用小波变换分解图像得到的低频部分进行编码,然后对图像高频部分采用分形编码,试图对图像压缩领域寻求一种新方法。     

基于小波变换的快速分形图像编码

分形图像压缩编码方法以其高压缩比的潜在性能而在近年来倍受重视,但是分形方法还存在着一些问题亟待解决。本文所提出的基于小波变换的分形压缩方法不仅弥补了分形方法编码时间过长的缺点,而且还使分形方法高压缩比的优点,得以进一步发挥。     

基于分形和小波变换的自适应混合图像编码

待编码图像经过金字塔型离散小波变换后的系数在小波域内可以组成分层树状数据结构一个小波树，这些跨越不同分辨率的小波树之间存在一定的相似性，可以通过分形变换来描述，本文正是构造小波树的基础邮基于分形和小波变换的自适应混合图像压缩算法，实验证明，我们提出的图像压缩方法与ＪＰＥＧ相比，能够在相近的压缩比的情况下（６０：１～７０：１）使得重建图像的ＰＳＮＲ（〉２９．５ｄＢ）增加约５．４ｄＢ并且图像的主观视觉     

基于方向性小波子树分形图像编码的改进算法

本文在分析混合分形零树小波图像编码算法(FZW)优缺点的基础上，提出一种新的基于方向性小波子树的分形图像编码算法。该算法结合零树小波编码和分形编码，通过在匹配搜索过程中使用方向性range和domain子树，提高匹配精度，改善了传统分形小波图像压缩中的方块效应，更大限度的保留了图像的边缘信息。实验结果表明，该算法在提高压缩比和去除图像的方块效应方面，均取得了良好的效果。     

几种小波图像编码方法的比较

小波变换具有良好的时频或空频局部特性,特别适合于按照人眼视觉系统特性设计的图像/视频编码方案.随着MPEG-4和JPEG-2000等基于小波变换的图像/视频编码标准的制定和推广,小波编码技术将被广泛采用.目前小波图像编码的方法有很多,其中嵌入式编码方法得到了人们的广泛关注.本文首先介绍EZW、SPIHT、SPECK等七种小波编码器的原理和方法,然后对这几种小波编码器的性能进行了比较.     

小波分析应用于迭代分形和统计预测分形相结合的图像编码方法

本文讨论并实现了一种基于小波分析的迭代分形和统计预测分形相结合的图像编码压缩方案，对于典型的512×512×8的灰度图像达到了比特率为0．236bpp，峰值信噪比约30．18dB的结果     

基于小波与分形相结合的图像压缩优化算法

提出了一种基于小波与分形相结合的图像压缩优化算法.首先将图像进行小波分解后,对低频子带单独编码;在分形编码时,根据不同方向子图的纹理特征不同,采用不同形状的块进行分形预测;同时对高频子带使用二次匹配编码,并且匹配运算只在同一方向上进行.实验证明,这种优化算法能够大大缩短分形编码的时间,并取得较高的压缩比.     

基于金字塔正交小波分解的快速分形图像编码

本文提出一种基于小波分解的快速分形图像编码方法。     

基于提升小波变换的快速分形图像编码

介绍了分形理论和提升小波变换理论,提出提升小波变换结合快速分形编码的混合编码方法。实验结果表明,提出的新算法在解码图像质量略有损失的情况下,图像编码时间和压缩比方面均有良好的效果。     

Hybrid fractal zerotree wavelet image coding

In this paper, a hybrid fractal zerotree wavelet (FZW) image coding algorithm is proposed. The algorithm couples a zerotree-based encoder, such as the embedded zerotree wavelet (EZW) coder or set partitioning in hierarchical trees, and a fractal image coder; this coupling is done in the wavelet domain. Based on perceptually-weighted distortion-rate calculations, a fractal method is adaptively applied to the parts of an image that can be encoded more efficiently relative to an EZW coder at a given rate. In addition to improving compression performance, the proposed algorithm also allows one to impose desirable properties from each type of image coder, such as progressive transmission, the zerotree structure, and range-domain block decoding.     

一种新的小波分形变换图像编码算法

提出了一种基于平滑双正交小波和自适应分割算法的小波域分形图像编码算法，在基于离散有限方差(DFV)最优准则下得到了适合图像编码的一种新的平滑双正交小波，从而改善了分块效应。在小波域的分形编码中，提出了一种基于图像信息分布特征的自适应分割算法，实验表明，该文算法在相同压缩比的情况下，解码图像的主观视觉质量和峰值信噪比都明显优于SQS方法、基本分形图像编码方法和SPIHT方法。     

Image coding using wavelet transform

A scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed. This method involves two steps. First, a wavelet transform used in order to obtain a set of biorthogonal subclasses of images: the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image. Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using a multiresolution codebook. To encode the wavelet coefficients, a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed. In order to allow the receiver to recognize a picture as quickly as possible at minimum cost, a progressive transmission scheme is presented. It is shown that the wavelet transform is particularly well adapted to progressive transmission.     

A NEW FRACTAL ZEROTREE CODING FOR WAVELET IMAGE

Based on the mechanisms underlying the performance of fractal and Discrete Wavelet Transform(DWT), one method using fractal-based self-quantization coding way to code different subband coefficients of DWT is presented. Within this method finer coefficients are fractal encoded according to the successive coarser ones. Self-similarities inherent between parent and their children at the same spatial location of the adjacent scales of similar orientation are exploited to predict variation of information across wavelet scales. On the other hand, with respect to Human Visual System(HVS) model, we assign different error thresholds to different decomposition scales, and different shape of range blocks to different orientations of the same scale, by which the perceptually lossless high compression ratio can be achieved and the matching processing can be quickened dramatically.     

基于Wavelet的图像压缩编码

在图像处理过程中，压缩算法的编制是最为重要的部分，详细论述了利用小波变换来母缩彩色连续图像的技术细节，同时也对在舰蚴环境中实现该算法时遇到的问题做了分析，并给出了解答。     

Image coding using wavelet transform and classified vectorquantisation

The wavelet transform, which provides a multiresolution representation of images, has been widely used in image compression. A new image coding scheme using the wavelet transform and classified vector quantisation is presented. The input image is first decomposed into a hierarchy of three layers containing ten subimages by the discrete wavelet transform. The lowest resolution low frequency subimage is scalar quantised with 8 bits/pixel. The high frequency subimages are compressed by classified vector quantisation to utilise the crosscorrelation among different resolutions while reducing the edge distortion and computational complexity. Vectors are constructed by combining the corresponding wavelet coefficients of different resolutions in the same orientation and classified according to the magnitude and the position of wavelet transform coefficients. Simulation results show that the proposed scheme has a better performance than those utilising current scalar or vector quantisation schemes     

Image coding using wavelet transforms and entropy-constrainedtrellis-coded quantization

The discrete wavelet transform has recently emerged as a powerful technique for decomposing images into various multi-resolution approximations. Multi-resolution decomposition schemes have proven to be very effective for high-quality, low bit-rate image coding. In this work, we investigate the use of entropy-constrained trellis-coded quantization (ECTCQ) for encoding the wavelet coefficients of both monochrome and color images. ECTCQ is known as an effective scheme for quantizing memoryless sources with low to moderate complexity, The ECTCQ approach to data compression has led to some of the most effective source codes found to date for memoryless sources. Performance comparisons are made using the classical quadrature mirror filter bank of Johnston and nine-tap spline filters that were built from biorthogonal wavelet bases. We conclude that the encoded images obtained from the system employing nine-tap spline filters are marginally superior although at the expense of additional computational burden. Excellent peak-signal-to-noise ratios are obtained for encoding monochrome and color versions of the 512x512 "Lenna" image. Comparisons with other results from the literature reveal that the proposed wavelet coder is quite competitive.     

Image coding using dual-tree discrete wavelet transform

In this paper, we explore the application of 2-D dual-tree discrete wavelet transform (DDWT), which is a directional and redundant transform, for image coding. Three methods for sparsifying DDWT coefficients, i.e., matching pursuit, basis pursuit, and noise shaping, are compared. We found that noise shaping achieves the best nonlinear approximation efficiency with the lowest computational complexity. The interscale, intersubband, and intrasubband dependency among the DDWT coefficients are analyzed. Three subband coding methods, i.e., SPIHT, EBCOT, and TCE, are evaluated for coding DDWT coefficients. Experimental results show that TCE has the best performance. In spite of the redundancy of the transform, our DDWT _ TCE scheme outperforms JPEG2000 up to 0.70 dB at low bit rates and is comparable to JPEG2000 at high bit rates. The DDWT _TCE scheme also outperforms two other image coders that are based on directional filter banks. To further improve coding efficiency, we extend the DDWT to an anisotropic dual-tree discrete wavelet packets (ADDWP), which incorporates adaptive and anisotropic decomposition into DDWT. The ADDWP subbands are coded with TCE coder. Experimental results show that ADDWP _ TCE provides up to 1.47 dB improvement over the DDWT _TCE scheme, outperforming JPEG2000 up to 2.00 dB. Reconstructed images of our coding schemes are visually more appealing compared with DWT-based coding schemes thanks to the directionality of wavelets.     

Region-based wavelet coding methods for digital mammography

Spatial resolution and contrast sensitivity requirements for some types of medical image techniques, including mammography, delay the implementation of new digital technologies, namely, computer-aided diagnosis, picture archiving and communications systems, or teleradiology. In order to reduce transmission time and storage cost, an efficient data-compression scheme to reduce digital data without significant degradation of medical image quality is needed. In this study, we have applied two region-based compression methods to digital mammograms. In both methods, after segmenting the breast region, a region-based discrete wavelet transform is applied, followed by an object-based extension of the set partitioning in hierarchical trees (OB-SPIHT) coding algorithm in one method, and an object-based extension of the set partitioned embedded block (OB-SPECK) coding algorithm in the other. We have compared these specific implementations against the original SPIHT and the new standard JPEG 2000, both using reversible and irreversible filters, on five digital mammograms compressed at rates ranging from 0.1 to 1.0 bit per pixel (bbp). Distortion was evaluated for all images and compression rates by the peak signal-to-noise ratio. For all images, OB-SPIHT and OB-SPECK performed substantially better than the traditional SPIHT and JPEG 2000, and a slight difference in performance was found between them. A comparison applying SPIHT and the standard JPEG 2000 to the same set of images with the background pixels fixed to zero was also carried out, obtaining similar implementation as region-based methods. For digital mammography, region-based compression methods represent an improvement in compression efficiency from full-image methods, also providing the possibility of encoding multiple regions of interest independently.     

Image compression using bit-plane coding of wavelet coefficients

The authors propose new simple image coder based on a discrete wavelet transform (DWT). The DWT coefficients are coded in bit-planes. They use an improved version of the JBIG bi-level image compression method to code the DWT coefficient bit-planes. The experimental results are shown, both in distortion measurement and visual comparison, and are very promising