Image compression by vector quantization: a review focused on codebook generation

Recently, vector quantization (VQ) has received considerable attention, and has become an effective tool for image compression. It provides a high compression ratio and a simple decoding process. However, studies on the practical implementation of VQ have revealed some major difficulties such as edge integrity and codebook design efficiency. After reviewing the state-of-the-art in the field of vector quantization, we focus on iterative and non-iterative codebook generation algorithms.     

基于改进人工蜂群的图像矢量量化码书设计算法

为了进一步提高图像矢量量化的码书质量,提出了一种新的图像压缩矢量量化码书设计算法。该算法采用均方误差(MSE)作为码书设计的适应度函数,利用改进的人工蜂群算法进行适应度函数的优化求解,增强了算法的自组织性和收敛性,大大减少了陷入局部收敛的可能性。将一种基于和值特性的快速码字搜索思想引入到码书设计算法中,使算法计算量明显降低。仿真结果表明,该算法具有计算时间短、收敛速度快的优点,并且生成的码书质量好、稳定性强。     

矢量量化中码书旋转压缩的研究

普通码书中的码字之间在不同的方向上具有很大的相关性,存在大量的数据冗余。提出了将码书中的码字旋转压缩的理论。该理论是将各个码字按四个方向垂直旋转后进行相似性检查。如果旋转后的码字其中一个方向上与前面的码字存在相似,则将该码字删除,从而达到压缩的目的。编码时将压缩后的码书旋转恢复后进行编码,从而大幅降低了需要存储的码字数量。同时给出了一种将现有1 024阶16维码书旋转压缩成256阶16维的方法,并对该方法得到的码书性能进行了仿真验证。实验结果表明使用压缩后的码书在硬件实现时与普通的矢量量化码书相比减少了75%的存储空间和输入带宽,而PSNR平均只降低0.28 dB。     

基于新的空胞腔策略的矢量量化码书设计方法

为了克服传统LBG算法中的空胞腔现象,提出了一种基于码字间距最大化的新的空胞腔策略。利用离码书距离最大的输入矢量来修改胞腔中的码字,旨在形成码字的合理分布,减小矢量量化的平均失真。实验结果表明：提出的策略能有效地消除空胞腔现象,获得性能较好的码书,其峰值信噪比比传统的LBG算法提高了3 dB。     

基于向量量化的图像超分辨率方法

受分形编码思想启发,提出了一种新的基于向量量化的图像超分辨率方法。该方法使用学习算法来获取单幅输入图像中的高频信息和低频信息之间的对应关系,并利用此关系对输入图像的一个倍频程的空间频率内添加图像细节以获得高分辨率图像。该方法克服了传统插值方法中因过度平滑导致图像模糊和纹理保持较差的缺点,能够重现出传统插值方法不能复原出的一些高频图像细节。实验结果显示该算法在客观和主观上都比传统插值方法有更好的评价。     

矢量量化的初始码书算法

矢量量化的初始码书设计是很重要的,影响或决定着其后码书形成算法的迭代次数和最终的码书质量。针对原有的初始码书算法在性能上随机性强与信源匹配程度不高的问题,提出一种对于训练矢量实施基于分量的和值排序,然后做分离平均的初始码书形成算法。算法使用了矢量的特征量,脱离了对于图像结构因数的依赖,能产生鲁棒性较好的初始码书。实验证明了该方法的有效性,与LBG算法结合可进一步提高码书质量。     

基于图像分类的矢量量化数字水印算法*

通过提取熵和标准差两个特征对图像进行分类,将分类结果采用PNN算法进行矢量量化,将其生成的码书作为LBG算法的初始码书以降低该算法对初始码书敏感的缺点。同时在水印提取过程中,首先对码书进行预处理以减少图像攻击对码书的影响,再提取水印图像。实验证明,使用该方法不仅得到了较高的图像质量,而且对常见的攻击也具有鲁棒性。另外,在传输过程中对码书和索引值的窜改,也具有一定的抗攻击性。     

矢量量化用于颜色图像检索的改进方法

为了消除基于颜色的图像检索在颜色空间分布描述方向的不足,提出一种新的基于颜色的检索方法。引入颜色转移矩阵描述颜色的空间分布,再结合颜色直方图和颜色转移矩阵进行复合图像检索。同时通过矢量量化方法量化图像颜色得到颜色直方图和颜色转移矩阵,实现了在压缩领域进行图像检索,减少了额外计算负担。实验表明,该方法能有效提高检索效率和精度。     

一种基于自适应分块八叉树颜色量化的图像压缩技术

颜色量化(CQ)是减少图像颜色数量的过程,已广泛用于图像压缩。基于八叉树的颜色量化(OCQ)因其编码效率高、内存使用低和调色板选择效果良好而被认为是最流行的CQ算法之一。然而,OCQ应用的一个严峻挑战是如何有效地管理关键的本地颜色。提出了一种基于分块的自适应八叉树颜色量化(AB-OCQ)算法,实验结果表明,与传统的OCQ算法相比,由于增加了对局部颜色的适当处理,AB-OCQ可以显著提高图像质量。在图像压缩比方面,AB-OCQ的综合性能也优于OCQ的。同时,和主流图像文件格式相比,AB-OCQ算法可以在保持压缩的前提下拥有随机访问图像像素数据的特性,该特性能让应用程序在同等内存下存储更多的图像数据,为提高应用程序的效率提供了一种方法。     

一种基于神经网络的网格实时调度算法*

提出一种在指定的最终期限内,利用队列技术来模拟调度动态资源,构建一个数学神经模型调度应用的子任务,使用GridSim 工具测试的调度算法,通过大约90%的模拟实验说明了模型调度任务是成功和高效的。     

Improved batch fuzzy learning vector quantization for image compression

In this paper, we develop a batch fuzzy learning vector quantization algorithm that attempts to solve certain problems related to the implementation of fuzzy clustering in image compression. The algorithm’s structure encompasses two basic components. First, a modified objective function of the fuzzy c-means method is reformulated and then is minimized by means of an iterative gradient-descent procedure. Second, the overall training procedure is equipped with a systematic strategy for the transition from fuzzy mode, where each training vector is assigned to more than one codebook vectors, to crisp mode, where each training vector is assigned to only one codebook vector. The algorithm is fast and easy to implement. Finally, the simulation results show that the method is efficient and appears to be insensitive to the selection of the fuzziness parameter.     

High-capacity image hiding scheme based on vector quantization

A novel grayscale image hiding scheme that is capable of hiding multiple secret images into a host image of the same size is proposed in this paper. The secret images to be hidden are first compressed by vector quantization with additional index compression process. Then, the compressed secret images are encrypted and embedded into the least-significant bits of the host pixels. To provide good image quality of the stego-image, the modulus function and the image property are employed to hide the secret bits into the host pixels and determine the number of hidden bits in each host pixel, respectively. According to the results, the proposed scheme provides a higher hiding capacity and a higher degree of security than that of the virtual image cryptosystem.     

Machine fusion to enhance the topology preservation of vector quantization artificial neural networks

Artificial neural networks techniques have been successfully applied in vector quantization (VQ) encoding. The objective of VQ is to statistically preserve the topological relationships existing in a data set and to project the data to a lattice of lower dimensions, for visualization, compression, storage, or transmission purposes. However, one of the major drawbacks in the application of artificial neural networks is the difficulty to properly specify the structure of the lattice that best preserves the topology of the data. To overcome this problem, in this paper we introduce merging algorithms for machine-fusion, boosting-fusion-based and hybrid-fusion ensembles of SOM, NG and GSOM networks. In these ensembles not the output signals of the base learners are combined, but their architectures are properly merged. We empirically show the quality and robustness of the topological representation of our proposed algorithm using both synthetic and real benchmarks datasets.     

Drill flank wear estimation using supervised vector quantization neural networks

Drill wear detection and prognosis is one of the most important considerations in reducing the cost of rework and scrap and to optimize tool utilization in hole making industry. This study presents the development and implementation of two supervised vector quantization neural networks for estimating the flank-land wear size of a twist drill. The two algorithms are; the learning vector quantization (LVQ) and the fuzzy learning vector quantization (FLVQ). The input features to the neural networks were extracted from the vibration signals using power spectral analysis and continuous wavelet transform techniques. Training and testing were performed under a variety of speeds and feeds in the dry drilling of steel plates. It was found that the FLVQ is more efficient in assessing the flank wear size than the LVQ. The experimental procedure for acquiring vibration data and extracting features in the time-frequency domain using the wavelet transform is detailed. Experimental results demonstrated that the proposed neural network algorithms were effective in estimating the size of the drill flank wear.     

Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach

Most industrial processes exhibit inherent nonlinear characteristics. Hence, classical control strategies which use linearized models are not effective in achieving optimal control. In this paper an Artificial Neural Network (ANN) based reinforcement learning (RL) strategy is proposed for controlling a nonlinear interacting liquid level system. This ANN-RL control strategy takes advantage of the generalization, noise immunity and function approximation capabilities of the ANN and optimal decision making capabilities of the RL approach. Two different ANN-RL approaches for solving a generic nonlinear control problem are proposed and their performances are evaluated by applying them to two benchmark nonlinear liquid level control problems. Comparison of the ANN-RL approach is also made to a discretized state space based pure RL control strategy. Performance comparison on the benchmark nonlinear liquid level control problems indicate that the ANN-RL approach results in better control as evidenced by less oscillations, disturbance rejection and overshoot.     

基于矢量量化和深度优先搜索的轨迹分布模式学习算法

在对轨迹流矢量进行量化编码的基础上,提出了一种基于深度优先搜索的轨迹分布模式提取算法,生成了能够描述轨迹分布的序列模式图,并给出了与之相应的异常检测和行为预测方法。对不同场景的可见光和红外序列图像的实验表明,本文方法不仅能够学习轨迹中流矢量的分布,而且能够反映它们之间的时序关系,可以应用于室外复杂场景的目标异常行为检测。     

基于字典学习的卷积神经网络压缩方法

为解决深度卷积神经网络模型占用存储空间较大的问题,提出一种基于K-SVD字典学习的卷积神经网络压缩方法。用字典中少数原子的线性组合来近似表示单个卷积核的参数,对原子的系数进行量化,存储卷积核参数时,只须存储原子的索引及其量化后的系数,达到模型压缩的目的。在MNIST数据集上对LeNet-C5和CIFAR-10数据集上对DenseNet的压缩实验结果表明,在准确率波动不足0.1%的情况下,将网络模型占用的存储空间降低至12%左右。     

Image indexing and retrieval based on vector quantization

To effectively utilize information stored in a digital image library, effective image indexing and retrieval techniques are essential. This paper proposes an image indexing and retrieval technique based on the compressed image data using vector quantization (VQ). By harnessing the characteristics of VQ, the proposed technique is able to capture the spatial relationships of pixels when indexing the image. Experimental results illustrate the robustness of the proposed technique and also show that its retrieval performance is higher compared with existing color-based techniques.     

基于量化表的图像篡改检测算法

数字图像盲取证技术是近年来研究的一个热点问题。提出一种利用量化表来进行定位和检测数字图像双重JPEG压缩的篡改检测算法。首先对数字图像进行压缩消除图像本身的噪声,然后利用图像压缩模型来描述图像首次压缩与第二次压缩之间的关系进而估计首次压缩量化表,最后提出一种高效率的方法通过利用量化表来定位图像篡改区域。实验结果表明,该算法能够有效地对双重JPEG压缩的图像进行检测和定位,并具有很好的鲁棒性。