块间预测矢量量化编码

本文介绍了最近发展起来的矢量量化编码原理,并对在较高压缩率情况下,矢量量化编码存在的问题进行了分析,提出了块间预测矢量量化编码方法用于消除矢量量化所产生的块现象。计算机模拟表明:这种方法能够有效地抑制块现象。     

一种混合的四叉树分形图像编码

为克服四叉树分形编码中纹理信息丰富的图像块较难找到满足要求相似块的不足,在四叉树分形图像编码的基础上,提出了一种与BTC方法结合的混合分形编码方法。讨论了四叉树分形编码与BTC方法,综合了2种方法的优点,对四叉树分形编码中块尺寸为4×4且找不到满足要求相似块的图像块,采用BTC方法,并用标准图像进行仿真实验,给出了四叉树编码方法与本文方法的实验结果。实验结果表明:此方法能明显改善重构图像的MSE或PSNR指标。     

基于图像活动性的序列图像分形编码方法

介绍了分形的概念以及分形压缩的主要思想;阐述了传统的分形图像压缩编码的基本原理与实现方法;提出了一种基于图像活动性序列图像分形编码方法。该方法首先由相邻帧之间的预测差值来判断当前编码块的活动性,然后根据图像的活动性,对不同特性的块采用不同的分形编码策略,最后对编码后得到的迭代函数系统（IFS）码进行可变长度编码（VLC）,以获得更高的压缩比。     

一种基于分类矢量量化器的小目标红外图像压缩方法

本文提出了一种基于分类矢量量化器的小目标红外图像的压缩方法。首先利用图像子块的平均灰度与纹理能量这两个参数将图像划分为背景区域与感兴趣区域,然后分别对两类区域的子块进行码书设计,用相对较多的码字描述感兴趣区域,用相对较少的码字描述背景区域,这样既达到了较高的压缩比,同时又较好的保留了感兴趣区域的信息,并且编码计算量有大幅度的下降。文中对分类矢量量化器对于减小编码计算量的作用进行了理论分析。实验结果表明在相同码书尺寸的情况下本算法比直接矢量量化方法更好地保留了红外图像中的小目标信息,并且加快了编码速度。     

基于转动惯量的快速分形图像编码算法

针对基本分形编码方法编码时间过长的问题,基于新定义的图像块的转动惯量特征的概念提出一种基于转动惯量的快速算法.算法对码本按转动惯量大小赋序,再对每个需要匹配的Range块在赋序码本中寻找与它的转动惯量最接近的码块,即只在这个码块的邻域内搜索Range块的最佳匹配块.实验结果表明,较之基本分形编码算法,该算法编码时间大大缩短,图像质量没有受到太大影响并且优于形态特征的特征算法.     

基于Krawtchouk矩的自适应门限四叉树分形编码

编码时间过长是分形图像压缩存在的主要问题,同时固定匹配门限影响着编码效率.本文首次将Krawtchouk矩引入到自适应分形编码中,提出了基于Krawtchouk矩的自适应门限四叉树分形编码方法.该方法计算图像块的12个Krawtchouk矩不变量,将这些矩不变量构成特征向量,并用这样的特征向量取代Range块和Domain块的灰度值矩阵进行匹配计算.文中给出了实验结果,并与基于四叉树的自适应门限分形图像IFS压缩方法进行了比较.结果表明,本文方法不仅大幅度减少了编码时间,而且同时也能提高图像恢复质量.     

基于区域分割的分形图像压缩编码方法

在基于块分割的分形图像压缩编码研究基础上，提出了一种基于区域分割和十字搜索模型的分形图像压缩编码新方法，实验结果表明，该方法与基于块分割的分形图像压缩编码方法相比，在保持恢复图像质量的前提下，压缩比和编码速度都有了显著的提高，是一种有效的分形图像压缩编码方法。     

一种基于显示不相关检验的快速分形图像编码方法

针对分形编码计算量过大的缺点,本文提出一种基于显著不相关检验的分形编码方法,对于文中的测试图像,其编码速度比基于子块分发形编码方法快２－１５倍,而解码图像质量ＰＳＮＲ）没有明显下降,压缩比还有一定的提高。     

基于特征值苑配的变比特率相关矢量量化图像编码算法

该文提出了一种用于图像编码的新颖的变比特率相关矢量量化器。在编码之前,首先计算各码字的四个特征值,然后根据各特征值的升序排列得到相应的四个排序码书。在对当前输入矢量(当前处理图像块)进行编码的过程中,充分考虑当前处理图像块与其相邻图像块之间的相关性以及各码字特征值与该输入矢量特征值之间的匹配性。测试结果表明,该算法与传统矢量量化(VQ)器相比,虽然在编码质量上有少许下降,但降低了比特率并加快了编码速度。     

一种基于显著不相关检验的快速分形图像编码方法

针对分形编码计算量过大的缺点,本文提出一种基于显著不相关检验的分形编码方法。对于文中的测试图像（Lenn256×256×8ppb和Boat256×256×8ppb）,其编码速度比基于子块分类的分形编码方法快2～15倍,而解码图像质量（PSNR）没有明显下降,压缩比还有一定的提高。     

Vector quantization with hierarchical classification of sub-blocks

A hierarchical classified vector quantization (HCVQ) method is described. In this method, the image is coded in several steps, starting with a relatively large block size, and successively dividing the block into smaller sub-blocks in a quad-tree fashion. The initial block is first vector quantized in the normal way. Classified vector quantization is then performed for its sub-blocks using the vector index of the initial block, i.e. rough information of the image, and the location of the sub-block within the initial block as classifiers. The coding proceeds in a similar way, adding more information of the fine details at each level. The method is found to be effective and to give a good subjective quality. It is also simple to implement, leading to coding speeds typical to a tree search VQ.     

Hierarchical multirate vector quantization for image coding

The hierarchical multirate vector quantization (HMVQ) introduced in this paper is an improved form of vector quantization for digital image coding. The HMVQ uses block segmentation and a structure tree to divide an original image into several layers and sub-layers according to their grey scale contrast within blocks of a certain size. Variant bit-rates are used for block coding of different layers with the same codebook. The HMVQ technique provides high encoded image quality with very low bit-rates. The processing time for codebook generation is considerably reduced by using layer by layer optimization and subsampling in low detail regions. This technique also demonstrates flexibility of accurate reproduction in different detail regions.     

Still image coding based on vector quantization and fractalapproximation

In this paper, we propose a coding algorithm for still images using vector quantization (VQ) and fractal approximation, in which low-frequency components of an input image are approximated by VQ, and its residual is coded by fractal mapping. The conventional fractal coding algorithms indirectly used the gray patterns of an original image with contraction mapping, whereas the proposed fractal coding method employs an approximated and then decimated image as a domain pool and uses its gray patterns. Thus, the proposed algorithm utilizes fractal approximation without the constraint of contraction mapping. For approximation of an original image, we employ the discrete cosine transform (DCT) rather than conventional polynomial-based transforms. In addition, for variable blocksize segmentation, we use the fractal dimension of a block that represents the roughness of the gray surface of a region. Computer simulations with several test images show that the proposed method shows better performance than the conventional fractal coding methods for encoding still pictures.     

Image coding based on a fractal theory of iterated contractiveimage transformations

The author proposes an independent and novel approach to image coding, based on a fractal theory of iterated transformations. The main characteristics of this approach are that (i) it relies on the assumption that image redundancy can be efficiently exploited through self-transformability on a block-wise basis, and (ii) it approximates an original image by a fractal image. The author refers to the approach as fractal block coding. The coding-decoding system is based on the construction, for an original image to encode, of a specific image transformation-a fractal code-which, when iterated on any initial image, produces a sequence of images that converges to a fractal approximation of the original. It is shown how to design such a system for the coding of monochrome digital images at rates in the range of 0.5-1.0 b/pixel. The fractal block coder has performance comparable to state-of-the-art vector quantizers.     

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.     

Predictive Vector Quantization of Images

The purpose of this paper is to present new image coding schemes based on a predictive vector quantization (PVQ) approach. The predictive part of the encoder is used to partially remove redundancy, and the VQ part further removes the residual redundancy and selects good quantization levels for the global waveform. Two implementations of this coding approach have been devised, namely, sliding block PVQ and block tree PVQ. Simulations on real images show significant improvement over the conventional DPCM and tree codes using these new techniques. The strong robustness property of these coding schemes is also experimentally demonstrated.     

Regularized subband coding scheme

Two enhanced subband coding schemes using a regularized image restoration technique are proposed: the first controls the global regularity of the decompressed image; the second extends the first approach at each decomposition level. The quantization scheme incorporates scalar quantization (SQ) and pyramidal lattice vector quantization (VQ) with both optimal bit and quantizer allocation. Experimental results show that both the block effect due to VQ and the quantization noise are significantly reduced.     

Hierarchical finite-state vector quantization for image coding

The hierarchical finite-state vector quantization (HFSVQ) introduced in the paper is an improvement of the finite state vector quantization combined with hierarchical multirate image coding. Based on an understanding of the perception of human eye and the structural features of images, the HFSVQ technique employs different coding rates and different numbers of the predictive states for representative vector selection. The bit rate used to encode images is very low while the reconstructed images can still achieve a satisfactory perceptual quality     

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.     

Interframe hierarchical address-vector quantization

A new interframe coding technique called interframe hierarchical address-vector quantization (IHA-VQ) is presented. It exploits the local characteristics of a moving image's motion-compensated (via block matching) difference signals by using quadtree segmentation to divide each image into large, uniform regions and smaller, highly detailed regions. The detailed regions are encoded by vector quantization (VQ), and the larger, low-detail regions are replenished from the previous frame, IHA-VQ also exploits the correlation between the small blocks by encoding the addresses of neighboring vectors by using several codebooks of address code-vectors