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.     

Adaptive wavelet packet basis selection for zerotree image coding

Image coding methods based on adaptive wavelet transforms and those employing zerotree quantization have been shown to be successful. We present a general zerotree structure for an arbitrary wavelet packet geometry in an image coding framework. A fast basis selection algorithm is developed; it uses a Markov chain based cost estimate of encoding the image using this structure. As a result, our adaptive wavelet zerotree image coder has a relatively low computational complexity, performs comparably to state-of-the-art image coders, and is capable of progressively encoding images.     

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。     

Hybrid fractal image coding with quadtree-based progressive structure

A progressive structure which takes the quadtree depth into consideration is proposed for fractal image coding. Simulation results show that its image quality at different received data rates is better than that without considering the quadtree level. Then, a hybrid fractal image coding scheme based on traditional and no-search fractal image coding with the proposed progressive structure is suggested. The image quality and compression ratio can be controlled by a threshold, which makes it downward compatible to the no-search fractal image coding. Experimental results justify that the progressive performance of the proposed scheme is better than that of traditional fractal image coding.     

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。     

结合零树的分形运动补偿编码算法的研究

当前的视频编码算法无论是使用小波变换、分形理论还是运动补偿技术，使用单一的编码算法总是存在着压缩效率与实时视觉的矛盾。至今为止，已提出的小波分形混合编码方法也没能很好地利用小波变换后系数的统计特性，压缩的效果不论是在压缩比PSNR或是可伸缩性上并不理想，为此，本文提出了一种小波域内基于零树特点的分形运动补偿混合编码算法。实验证明，该算法具有良好的压缩性能，并且在重建图像和拥有逼真的视觉特件，获得了可伸缩的能够渐进的传输的编码码流。     

基于亚取样分形插值预测的混合图像编码方法

提出了一种基于亚取样分形插值预测的混合图像编码方法。将原始图像在水平方向和垂直方向皆作１／２抽取得到一幅“亚抽图像”，对亚抽图像进行分形编码得到亚抽图像的分形码，采用分形插值方法由亚抽图像的分形码解码得到原始图像的分形预测图像，然后对预测误差图像进行基于ＤＣＴ的自适应补偿编码。实验表明，这种方法的编码速度有了很大提高，而且恢复图像的质量具有较高的保真度。     

Universal perceptual weighted zerotree coding for image and videocompression

A universal representation for the perceptual weighted zerotree coding algorithm is developed, in which the perceptual weighted zerotree coding is decomposed into two separate parts, i.e. visual weighting and zerotree representation, which can be realised independently. Prior to zerotree processing, the extracted full-tree is weighted by using a visual weighting matrix. Any zerotree algorithm like EZW, SPIHT and zerotree space-frequency quantisation can be used to encode the weighted coefficients of the wavelet transform. In other words, any previous algorithm without perceptual weighting can be easily extended to form a new perceptual coder using the proposed framework. Several examples of visual weighting matrices are given to show the effect of the new method     

Dilation-run wavelet image coding

The run-length coding and the morphological representation are two classical schemes for wavelet image coding. The run-length coders have the advantage of simplicity by recording the lengths of zero-runs between significant wavelet coefficients but at the expense of yielding an inferior rate-distortion performance. The morphology-based coders, on the other hand, utilize the morphological dilation operation to delineate the clusters of significant coefficients for improving coding performance. In this paper, a novel dilation-run image coding algorithm is developed by taking the advantages of both schemes, in which the clustered significant coefficients are extracted by using the morphological dilation operation and the insignificant coefficients between the extracted clusters are coded by using the run-length coding method. The proposed dilation-run image coder is implemented in the framework of bitplane coding for producing embedded bitstreams. Compared with several state-of-the-art wavelet image coding methods, the proposed dilation-run image coding method achieves comparable rate-distortion coding performance, especially more attractive for fingerprint type of imageries.     

小波与分形相结合的图像编码方法

小波和分形都是新兴的图像编码方法,它们都突破了基于局域内相关去冗余的传统压缩方法的局限性,而一各有其优点,两种方法的结合,将更加发挥它们的潜力,文中介绍小波和分形结合的编码的最新进展,其中重点介绍了在ＰＰＣ方法。     

Efficient coding of sparse trees using an enhanced-embedded zerotree wavelet?algorithm

In the Embedded Zerotree Wavelet (EZW) algorithm, a large number of bits are consumed in the encoding of Isolated Zero (IZ) symbols. This is the main bottleneck of the EZW algorithm, which limits its performance in terms of compression gain. To circumvent this limitation of the EZW algorithm, we propose in this paper, the Enhanced-EZW (E-EZW) algorithm based on the novel concept of a sparse tree (ST) encoding scheme. The ST encoding scheme provides an efficient encoding of ‘IZ’ symbols and eventually gives significant improvement in compression gain. Image features are clustered at various locations in an image, which gives rise to spatial correlation between Significant Coefficients (SCs) at these locations. Based on the above observation, we further propose differential coding of relative position of SCs in ST (DCORPS) in the E-EZW (DCORPS E-EZW) algorithm. We analyze cases where the ST coding gives higher coding gain compared to the EZW algorithm. Further, we see that DCORPS in sparse tree coding improves the overall coding efficiency of the E-EZW algorithm. By simulation results, we also demonstrate that the E-EZW and DCORPS E-EZW algorithms outperform two other important wavelet-based compression algorithms: namely set partitioning in hierarchical trees (SPIHT) and JPEG-2000 for a representative set of real-life images.     

A NEW FRACTAL IMAGE CODING METHOD

Some shortcomings of common fractal image coding methods are studied , then they are corrected with a new method. The new method is improved further in DCT domain. Coding results show the advantage of the new method.     

Improved embedded zerotree wavelet coder

An improved embedded zerotree wavelet (IEZW) is presented, which can significantly reduce the scanning and symbol redundancy of the existing EZW     

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 nonlinear model for fractal image coding

After a very promising start, progress in fractal image coding has been relatively slow recently. Most improvements have been concentrating on better adaptive coding algorithms and on search strategies to reduce the encoding time. Very little has been-done to challenge the linear model of the fractal transformations used so far in practical applications. In this paper, we explain why effective nonlinear transformations are not easy to find and propose a model based on conformal mappings in the geometric domain that are a natural extension of the affine model. Our compression results show improvements over the linear model and support the hope that a deeper understanding of the notion of self-similarity would further advance fractal image coding.     

一种快速分形图像编码算法

目前,分形图像编码技术最主要的缺点仍然是编码时间太长.因此,如何提高分形图像编码速度成为当前分形图像编码技术的研究热点.本文从分形图像编码通用公式推导出一个不等式,利用此不等式,可以预先排除大量不可能与值域块匹配的定义域块,从而减少值域块与定义域块的匹配计算,以此达到缩短编码时间的目的.实验结果表明,在解码图像质量基本不变情况下,本文的方法所使用的编码时间比Fisher方案所需的编码时间减少了很多.     

A fractal vector quantizer for image coding

We investigate the relation between VQ (vector quantization) and fractal image coding techniques, and propose a novel algorithm for still image coding, based on fractal vector quantization (FVQ). In FVQ, the source image is approximated coarsely by fixed basis blocks, and the codebook is self-trained from the coarsely approximated image, rather than from an outside training set or the source image itself. Therefore, FVQ is capable of eliminating the redundancy in the codebook without any side information, in addition to exploiting the self-similarity in real images effectively. The computer simulation results demonstrate that the proposed algorithm provides better peak signal-to-noise ratio (PSNR) performance than most other fractal-based coders.     

Hierarchical quantization indexing for wavelet and wavelet packet image coding

In this paper, we introduce the quantization index hierarchy, which is used for efficient coding of quantized wavelet and wavelet packet coefficients. A hierarchical classification map is defined in each wavelet subband, which describes the quantized data through a series of index classes. Going from bottom to the top of the tree, neighboring coefficients are combined to form classes that represent some statistics of the quantization indices of these coefficients. Higher levels of the tree are constructed iteratively by repeating this class assignment to partition the coefficients into larger subsets. The class assignments are optimized using a rate-distortion cost analysis. The optimized tree is coded hierarchically from top to bottom by coding the class membership information at each level of the tree. Context-adaptive arithmetic coding is used to improve coding efficiency. The developed algorithm produces PSNR results that are better than the state-of-art wavelet-based and wavelet packet-based coders in literature.     

Predictive subband image coding with wavelet transform

Wavelet transform can decompose images into various multiresolution subbands. In these subbands the correlation exists. A novel technique for image coding by taking advantage of the correlation is addressed. It is based on predictive edge detection from the LL band of the lowest resolution level to predict the edge in the LH, HL and HH bands in the higher resolution level. If the coefficient is predicted as an edge it is preserved; otherwise, it is discarded. In the decoder, the location of the preserved coefficients can also be found as in the encoder. Therefore, no overhead is needed. Instead of complex vector quantization, which is commonly used in subband image coding for high compression ratio, simple scalar quantization is used to code the remaining coefficients and achieves very good results.