Image Compression Based on Multistage Vector Quantization

This paper presents a new three-stage vector quantization system for the compression of images. It uses some simple schemes including error block classifier, search order coding (SOC), and index vector coding. The error block classifier preserves the edge blocks and discards the psychovisually redundant texture blocks in the last stage. The index vector coding encodes the combination of quantization indexes of the last two stages, and the SOC is used for encoding the quantization index of the first stage. The proposed system can achieve better compression performance than the conventional multistage vector quantization systems.     

Distortion Minimization with Fast Local Search for Fractal Image Compression

Optimal fractal image coding is an NP-hard combinatorial optimization problem, which consists of finding in a finite set of contractive affine mappings one whose unique fixed point is closest to the original image. Current fractal image schemes are based on a greedy suboptimal algorithm known as collage coding. In a previous paper, Hamzaoui, Hartenstein, and Saupe proposed a local search algorithm that iteratively improves an initial solution found by collage coding. For a standard fractal scheme based on quadtree image partitions, peak-signal-to-noise ratio (PSNR) gains are up to 0.8 dB. However, the algorithm is time-consuming because it involves many iteration steps, each of which requires the computation of the fixed point of an affine mapping. In this paper, we provide techniques that drastically reduce the complexity of the algorithm. Moreover, we show that the algorithm is also successful with a state-of-the-art fractal scheme based on highly adaptive image partitions.     

Two-Dimensional Adaptive Decimation and Interpolation Scheme for Low Bit-Rate Image Coding

A novel image compression scheme based on two-dimensional adaptive decimation is reported in this paper. In this approach, images are encoded with adaptive sampling along the horizontal and vertical directions, and decoded with an edge prediction interpolation algorithm. The method is capable of maintaining reasonable coding fidelity at low bit-rate with good visual quality. As only a small amount of computation is required in the encoding and decoding processes, the compression scheme can be implemented for real time operation with simple hardware and a small amount of memory storage. The proposed scheme had been applied in encoding images at bit-rates between 0.2 and 0.33 bpp and the results are encouraging.     

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.     

Combined Kohonen neural networks and discrete cosine transform method for iterated transformation theory

Iterated transformation theory (ITT) coding, also known as fractal coding, in its original form, allows fast decoding but suffers from long encoding times. During the encoding step, a large number of block best-matching searches have to be performed which leads to a computationally expensive process. Because of that, most of the research efforts carried on this field are focused on speeding up the encoding algorithm. Many different methods and algorithms have been proposed, from simple classifying methods to multi-dimensional nearest key search. We present in this paper a new method that significantly reduces the computational load of ITT-based image coding. Both domain and range blocks of the image are transformed into the frequency domain (which has proven to be more appropriate for ITT coding). Domain blocks are then used to train a two-dimensional Kohonen neural network (KNN) forming a codebook similar to vector quantization coding. The property of KNN (and self-organizing feature maps in general) which maintains the input space (transformed domain blocks) topology allows to perform a neighboring search to find the piecewise transformation between domain and range blocks.     

Image coding by block prediction of multiresolution subimages

The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but it has not been fully recognized and exploited for general images. Fractal block coders have exploited the self-similarity among blocks in images. We devise an image coder in which the causal similarity among blocks of different subbands in a multiresolution decomposition of the image is exploited. In a pyramid subband decomposition, the image is decomposed into a set of subbands that are localized in scale, orientation, and space. The proposed coding scheme consists of predicting blocks in one subimage from blocks in lower resolution subbands with the same orientation. Although our prediction maps are of the same kind of those used in fractal block coders, which are based on an iterative mapping scheme, our coding technique does not impose any contractivity constraint on the block maps. This makes the decoding procedure very simple and allows a direct evaluation of the mean squared error (MSE) between the original and the reconstructed image at coding time. More importantly, we show that the subband pyramid acts as an automatic block classifier, thus making the block search simpler and the block matching more effective. These advantages are confirmed by the experimental results, which show that the performance of our scheme is superior for both visual quality and MSE to that obtainable with standard fractal block coders and also to that of other popular image coders such as JPEG.     

机动目标的逆合成孔径雷达成像原理与算法

对于非合作的机动目标,由于目标相对于雷达射线的姿态和转速难以测定,而且是时变的,因而给逆合成孔径雷达(ISAR)成像造成较大困难.本文讨论了这种情况下成像的一般原理,并对机动性不太大,散射点子回波多普勒变化满足一阶近似条件时,提出了实用算法.实测数据的处理结果说明新算法是可行的.     

一种基于方差的匹配方法

文章就分形图象压缩中搜索与匹配过程的相似性提出了一个经过不同对比度实现快速分形编码算法，基于序列块和主块之间当前最小象差的方差信息，该算法排除不必搜索的主块，这样大大减少了每个序列块进行搜索和匹配的主块数与相应的编码时间，该算法在减少的时间内生成了与常规满搜索近乎一致的分形编码。     

Fast fractal image block coding based on local variances

In fractal image block coding, most of the time is spent on finding a close match between a range block and a large pool of domain blocks. For a large image, this effect becomes aggravated as the domain pool increases exponentially. We propose using the local variances of domain blocks to reduce the search space. By sorting the contracted domain pool according to their local variances and defining an acceptance criterion for a close match, we can confine all the potential close matches to a relatively small sized window to limit the search space. The encoding time can hence be shortened with the decoded image quality as good as that using the full search method. The speedup can be over ten times depending on the complexity of encoded images.     

基于分形的图像修复算法

 图像修复是目前图像处理领域中的一个研究热点,对于较大孔洞的修复一直是个难点问题,已有算法都未能很好地解决.本文基于分形相关理论,提出了一种新的修复算法,很好地利用了图像的整体信息.论述了分形维数和分形编码序列块大小之间的关系,提出多尺度的分形编码及重构的修复方法.为了强化图像细节信息,进行了分形局部迭代.为了提高图像修复的质量,将图像进行了分形放大,再进行分形插值修复.从实验结果可以看出,新方法取得了较好的修补效果,尤其是对纹理图像和有较大孔洞的图像效果更好.     

Content-based image retrieval using block-constrained fractalcoding and nona-tree decomposition

Fractal coding has been proved useful for image compression. In fractal coding, an image is represented by a number of self-transformations (fractal code) by which an approximation of the original image can be reconstructed. The authors present a block-constrained fractal coding scheme and a nona-tree decomposition based matching strategy for content-based image retrieval. In the coding scheme, an image is partitioned into non-overlapped blocks with a size close to that of a query iconic image. The fractal code is generated for each block independently. In the similarity measure of the fractal code, an improved nona-tree decomposition scheme is adopted to avoid matching the fractal code globally in order to reduce computational complexity. The experimental results show that the authors' coding scheme and matching strategy are useful for image retrieval, and compare favourably with two other methods tested in terms of storage usage and computing time     

使用区域搜索的DCT域分形图像编码方法

为降低ＤＣＴ域分形图像压缩的编码复杂度,文中引入了平坦块的概念,对那些经过ＤＣＴ变换后,能量非常集中的块不做块匹配,而是直接编码输出。同时经统计发现,对于给定的区块,与它匹配最好的域块通常位于这个区块的上方或附近,文中利用这个事实构造了一种优化的自适应搜索方案,大大减少了编码所需要的时间。实验表明,与一般的ＤＣＴ域分形图像压缩方法相比,文中方案在提高了编码速度的同时降低了比特率。     

FRACTAL BLOCK CODING IN RESIDUE DOMAIN

A new block-based fractal image coding algorithm called Fractal Block Coding in Residue Domain (FBCRD) is proposed. In basic Fractal Block Coding (FBC) algorithm, each block (called range block) is encoded by an affine mapping from a domain block within the same image to itself. The decoder uses the parameters of these mappings to synthesize the reconstructed image through an iterative procedure. FBCRD is a modification of basic FBC. In FBCRD, range blocks and domain blocks are all residue blocks subtracted from their block means and both the parameters of affine mappings and block means are coded. This modification leads to fewer iterations at the decoder. An optimized decoding strategy is also introduced which reduces total decoding time by more than half of that of basic FBC. This improvement is favorable for real time implementation of fractal image compression. Supported in Part by the Defence Preresearch Foundation, the National Science Foundation of Guangdong Prooince and the National “Chinbing” Project     

Novel image compression method using edge-oriented classifier andnovel predictive noiseless coding method

A new image compression approach is proposed in which variable block size technique is adopted, using quadtree decomposition, for coding images at low bit rates. In the proposed approach, low-activity regions, which usually occupy large areas in an image, were coded with a larger block size and the block mean is used to represent each pixel in the block. To preserve edge integrity, the classified vector quantisation (CVQ) technique is used to code high-activity regions. A new edge-oriented classifier without employing any thresholds is proposed for edge classification. A novel predictive noiseless coding (NPNC) method which exploits the redundancy between neighbouring blocks is also presented to efficiently code the mean values of low-activity blocks and the addresses of edge blocks. The bit rates required for coding the mean values and addresses can be significantly reduced by the proposed NPNC method. Experimental results show that excellent reconstructed images and higher PSNR were obtained     

Fractal image compression with region-based functionality

Region-based functionality offered by the MPEG-4 video compression standard is also appealing for still images, for example to permit object-based queries of a still-image database. A popular method for still-image compression is fractal coding. However, traditional fractal image coding uses rectangular range and domain blocks. Although new schemes have been proposed that merge small blocks into irregular shapes, the merging process does not, in general, produce semantically-meaningful regions. We propose a new approach to fractal image coding that permits region-based functionalities; images are coded region by region according to a previously-computed segmentation map. We use rectangular range and domain blocks, but divide boundary blocks into segments belonging to different regions. Since this prevents the use of standard dissimilarity measure, we propose a new measure adapted to segment shape. We propose two approaches: one in the spatial and one in the transform domain. While providing additional functionality, the proposed methods perform similarly to other tested methods in terms of PSNR but often result in images that are subjectively better. Due to the limited domain-block codebook size, the new methods are faster than other fractal coding methods tested. The results are very encouraging and show the potential of this approach for various internet and still-image database applications.     

Iteration-free fractal image coding based on efficient domain pooldesign

The domain pool design is one of the dominant issues which affect the coding performance of fractal image compression. In this paper, we employ the LBG algorithm and propose a block averaging method to design the efficient domain pools based on a proposed iteration-free fractal image codec. The redundancies between the generated domain blocks are reduced by the proposed methods. Therefore, we can obtain the domain pools that are more efficient than those in the conventional fractal coding schemes and thus the coding performance is improved. On the other hand, the iteration process in the conventional fractal coding scheme not only requires a large size of memory and a high computation complexity but also prolongs the decoding process. The proposed iteration-free fractal codec can overcome the problems above. In computer simulation, both the LBG-based and block-averaging methods for the domain pool design in the proposed iteration free scheme achieve excellent performances. For example, based on the proposed block-averaging method, the decoded Lena image has at least a 0.5 dB higher PSNR (under the same bit rate) and an eight-time faster decoding speed than the conventional fractal coding schemes that require iterations.     

基于自适应四叉树分形图像编码的数字水印技术

提出了一种基于自适应四叉树分形编码的数字水印技术。对原始图像进行四叉树分形编码后，通过给定子块在不同的寻找区域中搜索其最佳匹配父块来嵌入水印。实验结果表明，本方法在嵌入水印编码时间、图像质量和鲁棒性方面均比传统的方法有较大的改进。     

改善分形图像编码视觉效果的有效方法研究

针对当前分形图像编码面临如何改善重建图像视觉效果的问题,利用局部图像的特点,采取自适应的分块方法与缩短编码时间的多种块分类技术相结合设计图像编码算法,该算法明显改善了图像编码视觉效果,编码时间缩短上千倍,具有快速实现分形图像编码之功效。     

Gradient match and side match fractal vector quantizers for images

In this paper, we propose gradient match fractal vector quantizers (GMFVQs) and side match fractal vector quantizers (SMFVQs), which are two classes of finite state fractal vector quantizers (FSFVQs), for the image coding framework. In our previous work, we proposed the noniterative fractal block coding (FBC) technique to improve the decoding speed and the coding performance for conventional FBC techniques. To reduce the number of bits for denoting the fractal code of the range block, the concepts of the gradient match vector quantizers (GMVQs) and the side match vector quantizers (SMVQs) are employed to the noniterative FBC technique. Unlike ordinary vector quantizers, the super codebooks in the proposed GMFVQs and SMFVQs are generated from the affine-transformed domain blocks in the noniterative FBC technique. The codewords in the state codebook are dynamically extracted from the super codebook with the side-match and gradient-match criteria. The redundancy in the affine-transformed domain blocks is greatly reduced and the compression ratio can be significantly increased. Our simulation results show that 15%-20% of the bit rates in the noniterative FBC technique are saved by using the proposed GMFVQs.     

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。