基于BWT和FVQ的极低比特率图像编码算法

该文提出了一种基于双正交小波变换（ＢＷＴ）和模糊矢量量化（ＦＶＱ）的极低比特率图像编码算法。该算法通过构造符合图像小波变换系数特征的跨频带矢量,充分利用了不同频带小波系数之间的相关性,有效地提高了图像的编码效率和重构质量。     

基于9/7双正交小波的一种高效矢量量化算法

该文提出一种用于图像压缩的矢量量化算法,该算法用9/7双正交小波对图像进行分解,利用三个方向上各自小波系数之间的相关性,构造符合其特征的跨频带矢量,提高了图像的编码效率和重构质量,同时采用了新的矢量量化技术渐进构造聚类(PCC),实验结果证明,该算法在未熵编码的条件下,获得 PSNR32dB的重构图像,比特率高达 0.141 bpp.而且实现方法十分简单。     

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。     

基于可逆整数时间域重叠变换的分类矢量量化图像编码

针对普通矢量量化编码不能保留大量边缘细节信息,导致图像边缘细节模糊的问题,该文提出一种基于可逆整数时间域重叠变换(RTDLT)与分类矢量量化的图像压缩编码方法。首先对图像进行分块,同时对图像进行RTDLT变换,然后根据图像分块的梯度幅值与RTDLT变换系数对分块进行分类,最后对不同类别分块的RTDLT系数进行独立的基于模糊c均值矢量量化编码。实验证明,该算法比JPEG2000等其他算法具有更高的压缩倍数,重构图像质量更高。     

一种结合Contourlet和小波变换的图像编码算法

该文提出了一种Contourlet变换和小波变换相结合,使用新的空间方向树的类似SPIHT编码算法。该算法先对图像进行Contourlet变换,再对变换后的低频子带进行多级小波变换,然后根据变换后系数的结构特性,借鉴小波SPIHT编码思想,构造了一种新的空间方向树,实现了对变换后系数的类似SPIHT编码。仿真实验结果表明,该算法与小波变换,Contourlet变换和基于小波的Contourlet变换的SPIHT算法相比,重构图像保留了更多的纹理和细节信息,并且在低比特率下具有较高的峰值信噪比。     

一种基于小波变换和矢量量化的图像压缩算法

小波变换和矢量量化都是图像压缩中的重要方法。利用小波变换的系数特点,对图像进行小渡分解,对于能量最为集中的低频分量采用标量量化处理,然后将标量量化过程中产生的残差和高频分量一起构造矢量,进行矢量量化。实验结果表明,此算法能够有效提高重构图像质量,获得较高的信噪比。     

一个优化小波块量化矩阵的算法

小波变换以其良好的空间－频率局部特性,在图像编码标准JPEG2000和MPEG4中占据了重要位置.本文选用正交小波基对图像做小波变换,然后重新组织小波系数成小波块,最后提出了一个构造小波块量化矩阵以产生最优比特分配的算法.本算法用一种新的方式统计小波系数分布,并结合人体视觉系统的特点,采用动态策略在很大的比特率范围内产生最优的小波块量化矩阵.     

基于小波变换和误差竞争学习的矢量量化

用神经网络实现图像矢量量化是一种非常有效的方法，而小波变换又是近年来迅速发展起来的新算法。文中提出一种改进的误差竞争学习算法，分析了图像在小波变换后数据的特点，提出了新的矢量构造方法，从而最终得到了基于小波变换和误差竞争学习的矢量量化图像压缩新算法(以下简称VQWDCL)，无论是在主客观效果上，还是在计算复杂度上，其性能都优于传统的基于小波变换和LBG算法的矢量量化。     

基于小波分析的红外图像非线性增强算法

红外图像具有对比度低和信噪比低等特点,实际应用中需要进行增强处理。将小波分析与模糊逻辑相结合,提出了一种基于小波变换的红外图像非线性增强算法。该算法首先利用小波分析对图像进行分解,提取图像的多尺度特征信息;然后通过模糊非线性增强算子分别对各个分解层的子带系数进行运算以改变目标特征的强度;最后利用小波反变换重构图像,实现图像的对比度增强和背景抑制。与几种常用的红外图像增强算法进行了实验对比,验证了该算法的有效性。     

基于能量树编码的小波图像压缩算法

针对小波变换的空间能量聚集特性,提出了一种基于能量树编码的小波图像压缩算法。该算法在离散小波变换的基础上,分别对图像的各高频子带按其局部能量构建分层能量树,利用总能量和各层的能量角等效表示子带的小波系数;根据给定的压缩比,选择合适的代价函数构建最佳能量树,然后对其进行量化和编码,通过自适应的比特率分配实现小波图像压缩。实验结果表明,该算法实现简单,重构图像质量好,与当前多种主流的小波图像压缩算法相比,压缩性能有了明显提高。     

Finite-state vector quantization by exploiting interband andintraband correlations for subband image coding

Subband coding (SBC) with vector quantization (VQ) has been shown to be an effective method for coding images at low bit rates. We split the image spectrum into seven nonuniform subbands. Threshold vector quantization (TVQ) and finite state vector quantization (FSVQ) methods are employed in coding the subband images by exploiting interband and intraband correlations. Our new SBC-FSVQ schemes have the advantages of the subband-VQ scheme while reducing the bit rate and improving the image quality. Experimental results are given and comparisons are made using our new scheme and some other coding techniques. In the experiments, it is found that SBC-FSVQ schemes achieve the best peak signal-to-noise ratio (PSNR) performance when compared to other methods at the same bit rate.     

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.     

Hierarchical sub-band coding of super high definition image with adaptive block-size multistage VQ

A hierarchical image coding algorithm based on sub-band coding and adaptive block-size multistage vector quantization (VQ) is proposed, and its coding performance is examined for super high definition (SHD) image. First, the concept on SHD image is briefly described. Next, the signal power spectrum is evaluated, and the sub-band analysis pattern is determined from its characteristics. Several quadrature mirror filters are examined from the viewpoints of reconstruction accuracy, coding gain, and low-pass signal quality. Then an optimum filter is selected for the sub-band analysis. The two-stage VQ using the adaptive bit allocation is also introduced to control quantization accuracy and to achieve high-quality image reproduction. Coding performance and hierarchical image reconstruction are demonstrated using SNR and some photographs.     

Adaptive edge-based side-match finite-state classified vectorquantization with quadtree map

Vector quantization (VQ) is an effective image coding technique at low bit rate. The side-match finite-state vector quantizer (SMVQ) exploits the correlations between neighboring blocks (vectors) to avoid large gray level transition across block boundaries. A new adaptive edge-based side-match finite-state classified vector quantizer (classified FSVQ) with a quadtree map has been proposed. In classified FSVQ, blocks are arranged into two main classes, edge blocks and nonedge blocks, to avoid selecting a wrong state codebook for an input block. In order to improve the image quality, edge vectors are reclassified into 16 classes. Each class uses a master codebook that is different from the codebooks of other classes. In our experiments, results are given and comparisons are made between the new scheme and ordinary SMVQ and VQ coding techniques. As is shown, the improvement over ordinary SMVQ is up to 1.16 dB at nearly the same bit rate, moreover, the improvement over ordinary VQ can be up to 2.08 dB at the same bit rate for the image, Lena. Further, block boundaries and edge degradation are less visible because of the edge-vector classification. Hence, the perceptual image quality of classified FSVQ is better than that of ordinary SMVQ.     

Combined techniques of singular value decomposition and vectorquantization for image coding

The combination of singular value decomposition (SVD) and vector quantization (VQ) is proposed as a compression technique to achieve low bit rate and high quality image coding. Given a codebook consisting of singular vectors, two algorithms, which find the best-fit candidates without involving the complicated SVD computation, are described. Simulation results show that the proposed methods are better than the discrete cosine transform (DCT) in terms of energy compaction, data rate, image quality, and decoding complexity.     

一种结合人脸检测的小波图像编码方法

本文基于人脸检测的基础上，提出了一种结合矢量量化（ＶＱ）的小波图像编码方法。该方法充分利用人眼的视觉特性，高压缩比时恢复图像仍能保持较好的主观质量。     

Subband Finite-State Vector Quantization for Still Image Coding

Subband coding (SBC) with vector quantization (VQ) has been shown to be an effective method for coding images at low bit rates. The basic idea of subband coding is to split up the frequency band of the signal and then to encode the subbands. Reconstruction is performed by decoding and merging the interpolated subband images. In VQ, the image to be encoded is first processed to yield a set of vectors. The input vectors are individually quantized to the closest codewords in the codebook. In this paper, we propose a new subband finite-state vector quantization (SBC-FSVQ) scheme that combines the SBC and the FSVQ. The frequency band decomposition of an image is carried out by means of 2D separable quadrature mirror filters (QMFs). In our coding scheme, we split the image spectrum into sixteen equally sized subbands. The FSVQ is used to improve the performance by using the correlations of the neighboring samples in the same subband. Thus, our SBC-FSVQ scheme not only has the advantages of the SBC-VQ scheme but also reduces the bit rate and improves the image quality. Experimental results are given and comparisons are made using our new schemes and some other coding techniques. Our technique yields good PSNR performance, for images both inside and outside a training set of five 512 × 512 images. In the experiments, it is found that our SBC-FSVQ scheme achieves the best PSNR performance at nearly the same bit rate.     

用于图像编码的相关矢量量化研究

当相邻的图像块用矢量量化（ＶＱ）编码时可能出现编码地址相同的情况,尤其是在图像的平滑区。为了减少相邻块间编码地址的相关性,本文提出了一种相关矢量量化方案,采用相关码书与改进的自组织特征映射（ＩＳＯＦＭ）码书同时编码一个窗口内的四个邻域块,与无记忆类ＶＱ相比,对一幅典型的“Ｌｅｎｎａ”图象,编码过程中所需计算量减少一半,比特率减少４０％,由于在Ｋｏｈｏｎｅｎ自组织神经网络的训练过程中,对边缘类矢量采     

Conditional entropy coding of VQ indexes for image compression

Block sizes of practical vector quantization (VQ) image coders are not large enough to exploit all high-order statistical dependencies among pixels. Therefore, adaptive entropy coding of VQ indexes via statistical context modeling can significantly reduce the bit rate of VQ coders for given distortion. Address VQ was a pioneer work in this direction. In this paper we develop a framework of conditional entropy coding of VQ indexes (CECOVI) based on a simple Bayesian-type method of estimating probabilities conditioned on causal contexts, CECOVI is conceptually cleaner and algorithmically more efficient than address VQ, with address-VQ technique being its special case. It reduces the bit rate of address VQ by more than 20% for the same distortion, and does so at only a tiny fraction of address VQ's computational cost.